Dr. Ko joined Stanford Medicine in 2012 and serves as Director and Chief of Medical Dermatology for Stanford Health Care (SHC) while also spearheading the dermatology department's efforts around network development, digital health, quality/safety/performance improvement, and value-based care. He is active in a number of leadership roles within the organization including as an Associate Chief Quality Officer and physician dyadic partner to the Chief Experience Offer, as well as co-chair of the Clinic Advisory Council, a forum of medical and executive leaders of Stanford Health Care’s Ambulatory clinics.

His passion for melanoma, early cancer detection, and improving care delivery drives his efforts and research around leveraging advances in machine learning and artifical intelligence to increase the breadth of populations that can be reached. He developed and runs a digital care delivery program at SHC, providing virtual visits for patients and remote consultations for referring clinicians. He conducts research on and engages in collaborations around interventions that layer advances in machine learning on digital health capabilities to enhance access, quality and value of dermatologic care and is a founder and leader of the Stanford Translational AI in Dermatology (TRAIND) group. He chairs the American Academy of Dermatology's Committee on Augmented Intelligence.

Dr. Ko has also been driven to find new treatments for alopecia areata, an immune-mediated condition that can progress to total hair loss through various clinical trials and translational research efforts. He sits on the clinical research advisory board of the National Alopecia Areata Foundation and is a founder and co-director of the Skin Innovation and Interventional Research Group (SIIRG) which conducts clinical and translational research on skin disease.

He graduated magna cum laude from Harvard University and worked in investment banking; mergers and acquisitions at JP Morgan before going on to earn a combined medical and business degree at Tufts University. During medical school, he was a member of the Alpha Omega Alpha honor society. Dr. Ko then performed his residency at the Harvard Dermatology Residency Training Program where he served as chief resident.

Clinical Focus

  • Dermatology
  • Skin Cancer
  • Alopecia Areata
  • Telehealth
  • Psoriasis
  • Melanoma
  • Artificial Intelligence and Machine Learning
  • Screening for melanoma in high risk patients (family history, red hair, many moles)
  • Vitiligo
  • Laser Therapy of Medical Skin Conditions

Academic Appointments

Administrative Appointments

  • Associate Chief Quality Officer, Stanford Health Care (2021 - Present)
  • Physician Dyadic partner to Chief Experience Officer, Stanford Health Care (2021 - Present)
  • Co-Chair, Clinic Advisory Council, Stanford Health Care (2018 - Present)
  • Medical Director and Chief, Medical Dermatology, Stanford Health Care (2012 - Present)
  • Physician Leader, Safety, Quality and Performance Improvement, Stanford Department of Dermatology (2017 - Present)
  • Director of Network Development and Digital Health, Stanford Department of Dermatology (2012 - Present)
  • Director of Value Based Care, Stanford Department of Dermatology (2020 - Present)
  • Medical Director, Service Excellence, Stanford Health Care (2018 - Present)
  • Member, Ambulatory Value Optimization Committee, Stanford Health Care (2018 - Present)
  • Member, Access Management Guidance Team, Stanford Health Care (2018 - Present)
  • Member, Clinic Advisory Council, Stanford Hospital and Clinics (2015 - Present)
  • Physician Lead and Co-Chair, Clinic Performance Team 4, Stanford Hospital and Clinics (2015 - 2018)

Boards, Advisory Committees, Professional Organizations

  • Chair, American Academy of Dermatology Committee on Augmented Intelligence (2019 - Present)
  • Research Advisory Council, National Alopecia Areata Foundation (2016 - Present)

Professional Education

  • Board Certification: American Board of Dermatology, Dermatology (2012)
  • Chief Resident, Harvard Combined Dermatology Residency Training Program (2012)
  • Residency: Massachusetts General Hospital (2012) MA
  • BA, Harvard University, Magna Cum Laude (2002)
  • MD, MBA, Tufts University School of Medicine, Alpha Omega Alpha (2008)

Clinical Trials

  • A Study of Baricitinib (LY3009104) in Participants With Severe or Very Severe Alopecia Areata Recruiting

    This study is designed to select up to two doses of baricitinib (referred to as low dose and high dose) and assess their efficacy and safety for the treatment of severe or very severe alopecia areata. An additional subpopulation of 60 participants in the US will enroll in the open-label addenda.

    View full details

  • A Study to Evaluate Upadacitinib in Adolescents and Adults With Moderate to Severe Atopic Dermatitis (Measure Up 2) Recruiting

    The objective of this study is to assess the efficacy and safety of upadacitinib for the treatment of adolescent and adult participants with moderate to severe atopic dermatitis (AD) who are candidates for systemic therapy.

    View full details

  • A Study to Evaluate Upadacitinib in Combination With Topical Corticosteroids in Adolescent and Adult Participants With Moderate to Severe Atopic Dermatitis Recruiting

    The objective of this study is to assess the efficacy and safety of upadacitinib combined with topical corticosteroids (TCS) for the treatment of adolescent and adult participants with moderate to severe atopic dermatitis (AD) who are candidates for systemic therapy.

    View full details

  • Effect of Dupilumab (Anti-IL4Rα) on the Host-Microbe Interface in Atopic Dermatitis Recruiting

    The purpose of this study is to understand the effect that T helper 2 (Th2) blockade has on well-described pathophysiological features of Atopic Dermatitis (AD), for example: barrier, epidermal activation, dysbiosis and epidermal lipids.

    View full details

  • Extension Study to Evaluate Safety and Efficacy of CTP-543 in Adults With Alopecia Areata Recruiting

    The overall objectives of the study are to evaluate long-term safety of CTP-543 and to assess long-term effects of CTP-543 on treating hair loss in adult patients with chronic, moderate to severe alopecia areata.

    View full details

  • SAR231893-LPS15497- "Dupilumab Effect on Sleep in AD Patients" Recruiting

    Primary Objective: To evaluate the effect of dupilumab on sleep quality in adult participants with moderate to severe atopic dermatitis (AD). Secondary Objectives: To evaluate the effect of dupilumab on objective and subjective quantitative sleep parameters, AD related outcomes, and daytime consequences of sleep deprivation. To continue to assess the safety and tolerability throughout the study.

    View full details

  • Study to Evaluate the Safety and Efficacy of CTP-543 in Adults With Moderate to Severe Alopecia Areata Recruiting

    This study will evaluate the safety and efficacy of CTP-543 on hair loss in adults with chronic, moderate to severe alopecia areata.

    View full details

  • A Study of ATI-502 Topical Solution for the Treatment of Atopic Dermatitis Not Recruiting

    This is an open label, multicenter study designed to evaluate the safety and tolerability of ATI-502 Topical Solution in male and female subjects with moderate or severe atopic dermatitis (AD). Subjects will be required to apply ATI-502 study medication to their identified AD treatment areas. All subjects will be required to complete a safety follow up visit 4 weeks post last study medication application

    Stanford is currently not accepting patients for this trial.

    View full details

  • A Study of Lebrikizumab (LY3650150) in Participants With Moderate-to-Severe Atopic Dermatitis Not Recruiting

    The purpose of this study is to evaluate the safety and efficacy of lebrikizumab compared with placebo in participants with moderate-to-severe atopic dermatitis.

    Stanford is currently not accepting patients for this trial.

    View full details

  • Tofacitinib for the Treatment of Alopecia Areata and Its Variants Not Recruiting

    The purpose of this study is to investigate the ability of tofacitinib citrate, a Janus kinase inhibitor, to generate hair regrowth in patients with moderate to severe alopecia areata and its variants.

    Stanford is currently not accepting patients for this trial.

    View full details

2023-24 Courses

Stanford Advisees

All Publications

  • Clinical Outcomes for Uptitration of Baricitinib Therapy in Patients With Severe Alopecia Areata: A Pooled Analysis of the BRAVE-AA1 and BRAVE-AA2 Trials. JAMA dermatology Ko, J. M., Mayo, T. T., Bergfeld, W. F., Dutronc, Y., Yu, G., Ball, S. G., Somani, N., Craiglow, B. G. 2023


    Baricitinib is an oral selective Janus kinase 1/2 inhibitor that has achieved clinically meaningful outcomes for scalp, eyebrow, and eyelash hair regrowth in patients with severe alopecia areata (AA) at week 36 of treatment. Treatment with baricitinib, 4 mg, has resulted in higher response rates than baricitinib, 2 mg, at weeks 36 and 52.To determine the efficacy of uptitration to baricitinib, 4 mg, for 24 weeks in patients who had previously not responded to baricitinib, 2 mg (Severity of Alopecia Tool [SALT] score of >20).BRAVE-AA1 and BRAVE-AA2 are multicenter, placebo-controlled, phase 3 randomized clinical trials that were initiated on September 24, 2018, and July 8, 2019, respectively, with follow-up to 200 weeks (data cutoffs of November 11, 2021, and November 5, 2021, respectively). This pooled analysis reports long-term extension data up to week 76. At baseline, 1200 adult patients with severe AA (SALT score ≥50) were randomly assigned in a 3:2:2 ratio to receive baricitinib, 4 mg; baricitinib, 2 mg; or placebo. Patients treated with baricitinib remained on the same treatment dose until week 52. Patients were considered nonresponders to baricitinib, 2 mg, if they had a SALT score greater than 20 after 52 weeks of therapy.The proportions of patients achieving a SALT score of 20 or lower and clinician-reported outcome for eyebrow hair loss and eyelash hair loss scores of 0 or 1 (full coverage or minimal gaps) with 2-point or higher improvements from baseline (among those with baseline scores ≥2 [significant gaps to no notable hair]) were analyzed through week 76.At week 52, of the 340 patients (mean [SD] age, 38.4 [12.9] years; 212 [62.4%] female) treated with baricitinib, 2 mg, 212 (62.4%) had a SALT score higher than 20 and were uptitrated to baricitinib, 4 mg. Two-thirds of these patients (142 of 212 [67.0%]) had a baseline SALT score of 95 to 100, indicating very severe AA. At week 76, 55 of the 212 patients (25.9%) had achieved a SALT score of 20 or lower. During the same period, response rates for clinician-reported outcome scores of 0 or 1 increased from 19.3% (31 of 161 patients) to 37.9% (61 of 161 patients) for eyebrows and from 24.1% (33 of 137 patients) to 40.9% (56 of 137 patients) for eyelashes.In this pooled analysis of the BRAVE-AA1 and BRAVE-AA2 trials, uptitration of baricitinib, 2 mg, to baricitinib, 4 mg, in those who did not respond to the 2-mg dose resulted in meaningful improvement of response rates over the subsequent 24 weeks for scalp, eyebrow, and eyelash hair Identifiers: NCT03570749 and NCT03899259.

    View details for DOI 10.1001/jamadermatol.2023.2581

    View details for PubMedID 37556146

  • Development and Clinical Evaluation of an Artificial Intelligence Support Tool for Improving Telemedicine Photo Quality. JAMA dermatology Vodrahalli, K., Ko, J., Chiou, A. S., Novoa, R., Abid, A., Phung, M., Yekrang, K., Petrone, P., Zou, J., Daneshjou, R. 2023


    Importance: Telemedicine use accelerated during the COVID-19 pandemic, and skin conditions were a common use case. However, many images submitted may be of insufficient quality for making a clinical determination.Objective: To determine whether an artificial intelligence (AI) decision support tool, a machine learning algorithm, could improve the quality of images submitted for telemedicine by providing real-time feedback and explanations to patients.Design, Setting, and Participants: This quality improvement study with an AI performance component and single-arm clinical pilot study component was conducted from March 2020 to October 2021. After training, the AI decision support tool was tested on 357 retrospectively collected telemedicine images from Stanford telemedicine from March 2020 to June 2021. Subsequently, a single-arm clinical pilot study was conducted to assess feasibility with 98 patients in the Stanford Department of Dermatology across 2 clinical sites from July 2021 to October 2021. For the clinical pilot study, inclusion criteria for patients included being adults (aged ≥18 years), presenting to clinic for a skin condition, and being able to photograph their own skin with a smartphone.Interventions: During the clinical pilot study, patients were given a handheld smartphone device with a machine learning algorithm interface loaded and were asked to take images of any lesions of concern. Patients were able to review and retake photos prior to submitting, so each submitted photo met the patient's assumed standard of clinical acceptability. A machine learning algorithm then gave the patient feedback on whether the image was acceptable. If the image was rejected, the patient was provided a reason by the AI decision support tool and allowed to retake the photos.Main Outcomes and Measures: The main outcome of the retrospective image analysis was the receiver operator curve area under the curve (ROC-AUC). The main outcome of the clinical pilot study was the image quality difference between the baseline images and the images approved by AI decision support.Results: Of the 98 patients included, the mean (SD) age was 49.8 (17.6) years, and 50 (51%) of the patients were male. On retrospective telemedicine images, the machine learning algorithm effectively identified poor-quality images (ROC-AUC of 0.78) and the reason for poor quality (blurry ROC-AUC of 0.84; lighting issues ROC-AUC of 0.70). The performance was consistent across age and sex. In the clinical pilot study, patient use of the machine learning algorithm was associated with improved image quality. An AI algorithm was associated with reduction in the number of patients with a poor-quality image by 68.0%.Conclusions and Relevance: In this quality improvement study, patients use of the AI decision support with a machine learning algorithm was associated with improved quality of skin disease photographs submitted for telemedicine use.

    View details for DOI 10.1001/jamadermatol.2023.0091

    View details for PubMedID 36920380

  • Response: Commentary: Real-world post-deployment performance of a novel machine learning-based digital health technology for skin lesion assessment and suggestions for post-market surveillance. Frontiers in medicine Thomas, L., Hyde, C., Mullarkey, D., Greenhagh, J., Kalsi, D., Ko, J. 2024; 11: 1388422

    View details for DOI 10.3389/fmed.2024.1388422

    View details for PubMedID 38756949

    View details for PubMedCentralID PMC11097772

  • Telehealth Utilization and Associations in the United States During the Third Year of the COVID-19 Pandemic: Population-Based Survey Study in 2022. JMIR public health and surveillance Kim, J., Cai, Z. R., Chen, M. L., Onyeka, S., Ko, J. M., Linos, E. 2024; 10: e51279


    BACKGROUND: The COVID-19 pandemic rapidly changed the landscape of clinical practice in the United States; telehealth became an essential mode of health care delivery, yet many components of telehealth use remain unknown years after the disease's emergence.OBJECTIVE: We aim to comprehensively assess telehealth use and its associated factors in the United States.METHODS: This cross-sectional study used a nationally representative survey (Health Information National Trends Survey) administered to US adults (≥18 years) from March 2022 through November 2022. To assess telehealth adoption, perceptions of telehealth, satisfaction with telehealth, and the telehealth care purpose, we conducted weighted descriptive analyses. To identify the subpopulations with low adoption of telehealth, we developed a weighted multivariable logistic regression model.RESULTS: Among a total of 6252 survey participants, 39.3% (2517/6252) reported telehealth use in the past 12 months (video: 1110/6252, 17.8%; audio: 876/6252, 11.6%). The most prominent reason for not using telehealth was due to telehealth providers failing to offer this option (2200/3529, 63%). The most common reason for respondents not using offered telehealth services was a preference for in-person care (527/578, 84.4%). Primary motivations to use telehealth were providers' recommendations (1716/2517, 72.7%) and convenience (1516/2517, 65.6%), mainly for acute minor illness (600/2397, 29.7%) and chronic condition management (583/2397, 21.4%), yet care purposes differed by age, race/ethnicity, and income. The satisfaction rate was predominately high, with no technical problems (1829/2517, 80.5%), comparable care quality to that of in-person care (1779/2517, 75%), and no privacy concerns (1958/2517, 83.7%). Younger individuals (odd ratios [ORs] 1.48-2.23; 18-64 years vs ≥75 years), women (OR 1.33, 95% CI 1.09-1.61), Hispanic individuals (OR 1.37, 95% CI 1.05-1.80; vs non-Hispanic White), those with more education (OR 1.72, 95% CI 1.03-2.87; at least a college graduate vs less than high school), unemployed individuals (OR 1.25, 95% CI 1.02-1.54), insured individuals (OR 1.83, 95% CI 1.25-2.69), or those with poor general health status (OR 1.66, 95% CI 1.30-2.13) had higher odds of using telehealth.CONCLUSIONS: To our best knowledge, this is among the first studies to examine patient factors around telehealth use, including motivations to use, perceptions of, satisfaction with, and care purpose of telehealth, as well as sociodemographic factors associated with telehealth adoption using a nationally representative survey. The wide array of descriptive findings and identified associations will help providers and health systems understand the factors that drive patients toward or away from telehealth visits as the technology becomes more routinely available across the United States, providing future directions for telehealth use and telehealth research.

    View details for DOI 10.2196/51279

    View details for PubMedID 38669075

  • Dermatologists' perspectives and usage of large language models in practice- an exploratory survey. The Journal of investigative dermatology Gui, H., Rezaei, S. J., Schlessinger, D., Weed, J., Lester, J., Wongvibulsin, S., Mitchell, D., Ko, J., Rotemberg, V., Lee, I., Daneshjou, R. 2024

    View details for DOI 10.1016/j.jid.2024.03.028

    View details for PubMedID 38582369

  • Best Practices for Research in Virtual and Augmented Reality in Dermatology. The Journal of investigative dermatology Muralidharan, V., Tran, M. M., Barrios, L., Beams, B., Ko, J. M., Siegel, D. H., Bailenson, J. 2024; 144 (1): 17-23


    Virtual reality (VR) and augmented reality (AR) technologies have advanced rapidly in recent years. These cutting-edge technologies provide dermatology researchers, educators, proceduralists, and patients with opportunities in new scientific horizons. VR is a technology that facilitates immersive human experiences by allowing users to connect with various simulated environments through natural head and hand movements, whereas AR supplements a user's perception of their real environment with virtual elements. Despite technological advancements, there is limited literature on the methodological steps for conducting rigorous VR and AR research in dermatology. Effective storyboarding, user-driven design, and interdisciplinary teamwork play a central role in ensuring that VR/AR applications meet the specific needs of dermatology clinical and research teams. We present a step-by-step approach for their design, team composition, and evaluation in dermatology research, medical education, procedures, and habit formation strategies. We also discuss current VR and AR dermatology applications and the importance of ethical and safety considerations in deploying this new technology.

    View details for DOI 10.1016/j.jid.2023.10.014

    View details for PubMedID 38105083

  • Association between yearlong air pollution and moderate-severe atopic dermatitis: A United States cross-sectional claims analysis. JAAD international Trinh, P., Allerup, J. A., Li, S., Ko, J., Chen, J., Linos, E., Chiou, A. S. 2023; 13: 4-6

    View details for DOI 10.1016/j.jdin.2023.04.017

    View details for PubMedID 37592975

  • Development of a digital tool for home-based monitoring of skin disease for older adults. Skin health and disease van Egmond, S., Cai, Z. R., Nava, V., Joy de Vere Hunt, I., Rapaport, B. R., Ko, J., Chiou, A. S., Sarin, K., Tang, J., Zhang, L., Linos, E. 2023; 3 (5): e235


    We developed a digital tool for home-based monitoring of skin disease, our digital tool. In the current observational pilot study, we found that DORA is feasible to use in practice, as it has a high patient compliance, retention and satisfaction. Clinicans rated the photos generally good quality or perfect quality. These results show that the digital health tool DORA can easily be used by patients to send photos to their dermatologist, which could reduce unnecessary clinical visits. It may also be used in other settings where digital literacy barriers and unequal access to dermatologists contribute to healthcare disparities.

    View details for DOI 10.1002/ski2.235

    View details for PubMedID 37799368

    View details for PubMedCentralID PMC10549824

  • Hair Loss Profiles and Ritlecitinib Efficacy in Patients with Alopecia Areata: Post Hoc Analysis of the ALLEGRO Phase 2b/3 Study DERMATOLOGY AND THERAPY Thaci, D., Tziotzios, C., Ito, T., Ko, J., Karadag, A., Fang, H., Edwards, R. A., Bonfanti, G., Wolk, R., Tran, H., Law, E. 2023
  • Hair Loss Profiles and Ritlecitinib Efficacy in Patients with Alopecia Areata: Post Hoc Analysis of the ALLEGRO Phase 2b/3 Study. Dermatology and therapy Thaci, D., Tziotzios, C., Ito, T., Ko, J., Karadag, A. S., Fang, H., Edwards, R. A., Bonfanti, G., Wolk, R., Tran, H., Law, E. 2023


    INTRODUCTION: Ritlecitinib demonstrated efficacy in patients with alopecia areata (AA) in the ALLEGRO phase 2b/3 study (NCT03732807). However, hair loss presentation may vary based on location (e.g., scalp, eyebrow/eyelash, body). Here, we sought to identify distinct hair loss profiles at baseline and evaluate whether they affected the efficacy of ritlecitinib.METHODS: Patients with AA aged≥12years with≥50% scalp hair loss were randomized to daily ritlecitinib 10mg (assessed for dose ranging only), 30 or 50mg (±4-week, 200-mg loading dose), or placebo for 24weeks. Latent class analysis (LCA) identified hair loss profiles based on four baseline measurements: clinician-reported extent of scalp (Severity of Alopecia Tool score), eyebrow hair loss, eyelash hair loss, and patient-reported body hair loss. Logistic regression evaluated ritlecitinib (50 and 30mg) efficacy vs placebo using Patient Global Impression of Change (PGI-C) and Patient Satisfaction with Hair Growth (P-Sat; amount, quality, and overall satisfaction) responses at Week 24, adjusting for key covariates, including latent class membership.RESULTS: LCA identified five latent classes: (1) primarily non-alopecia totalis (AT; complete loss of scalp hair); (2) non-AT with moderate non-scalp involvement; (3) extensive scalp, eyebrow, and eyelash involvement; (4) AT with moderate non-scalp involvement;and (5) primarily alopecia universalis (complete scalp, face, and body hair loss). Adjusting for latent class membership, patients receiving ritlecitinib 30 or 50mg were significantly more likely to achieve PGI-C response (30mg: odds ratio, 8.62 [95% confidence interval, 4.42-18.08]; 50mg: 12.29 [6.29-25.85]) and P-Sat quality of hair regrowth (30mg: 6.71 [3.53-13.51]; 50mg: 8.17 [4.30-16.46]) vs placebo at Week 24. Results were similar for P-Sat overall satisfaction and amount of hair regrowth.CONCLUSION: Distinct and clinically relevant hair loss profiles were identified in ALLEGRO-2b/3 participants. Ritlecitinib was efficacious compared with placebo, independent of hair loss profile at baseline.TRIAL REGISTRATION: identifier, NCT03732807.

    View details for DOI 10.1007/s13555-023-00997-x

    View details for PubMedID 37707764

  • Safety Analysis of Baricitinib in Adult Patients with Severe Alopecia Areata From 2 Randomized Clinical Trials over a Median of 1.6 years and up to 3.6 Years of Exposure King, B., Ko, J., Piraccini, B., Shimomura, Y., Dutron, Y., Wu, W., Yang, F., Holzwarth, K., Chiasserini, C., Sinclair, R. MOSBY-ELSEVIER. 2023: AB220
  • Concurrent improvement in scalp hair and eyebrow or eyelash regrowth in patients with severe alopecia areata treated with baricitinib King, B., Ko, J., Senna, M., Tosti, A., Ohyama, M., Dutronc, Y., Chen, Y., Yu, G., Smith, S., Wu, W. MOSBY-ELSEVIER. 2023: AB29
  • Baseline distribution of eyebrow and eyelash loss by severity of scalp hair loss in phase 3 trials of baricitinib for alopecia areata Shapiro, J., Ko, J., Egeberg, A., Somani, N., Jedynak, J., Torisu-Itakura, H., Yu, G., Lu, N., Craiglow, B. MOSBY-ELSEVIER. 2023: AB127
  • Integrated single-cell chromatin and transcriptomic analyses of human scalp identify gene-regulatory programs and critical cell types for hair and skin diseases. Nature genetics Ober-Reynolds, B., Wang, C., Ko, J. M., Rios, E. J., Aasi, S. Z., Davis, M. M., Oro, A. E., Greenleaf, W. J. 2023


    Genome-wide association studies have identified many loci associated with hair and skin disease, but identification of causal variants requires deciphering of gene-regulatory networks in relevant cell types. We generated matched single-cell chromatin profiles and transcriptomes from scalp tissue from healthy controls and patients with alopecia areata, identifying diverse cell types of the hair follicle niche. By interrogating these datasets at multiple levels of cellular resolution, we infer 50-100% more enhancer-gene links than previous approaches and show that aggregate enhancer accessibility for highly regulated genes predicts expression. We use these gene-regulatory maps to prioritize cell types, genes and causal variants implicated in the pathobiology of androgenetic alopecia (AGA), eczema and other complex traits. AGA genome-wide association studies signals are enriched in dermal papilla regulatory regions, supporting the role of these cells as drivers of AGA pathogenesis. Finally, we train machine learning models to nominate single-nucleotide polymorphisms that affect gene expression through disruption of transcription factor binding, predicting candidate functional single-nucleotide polymorphism for AGA and eczema.

    View details for DOI 10.1038/s41588-023-01445-4

    View details for PubMedID 37500727

    View details for PubMedCentralID 4006068

  • Best Practices for Clinical Skin Image Acquisition in Translational Artificial Intelligence Research. The Journal of investigative dermatology Phung, M., Muralidharan, V., Rotemberg, V., Novoa, R. A., Chiou, A. S., Sadée, C. Y., Rapaport, B., Yekrang, K., Bitz, J., Gevaert, O., Ko, J. M., Daneshjou, R. 2023; 143 (7): 1127-1132


    Recent advances in artificial intelligence research have led to an increase in the development of algorithms for detecting malignancies from clinical and dermoscopic images of skin diseases. These methods are dependent on the collection of training and testing data. There are important considerations when acquiring skin images and data for translational artificial intelligence research. In this paper, we discuss the best practices and challenges for light photography image data collection, covering ethics, image acquisition, labeling, curation, and storage. The purpose of this work is to improve artificial intelligence for malignancy detection by supporting intentional data collection and collaboration between subject matter experts, such as dermatologists and data scientists.

    View details for DOI 10.1016/j.jid.2023.02.035

    View details for PubMedID 37353282

  • Rapid Reduction in Staphylococcus aureus in Atopic Dermatitis Subjects Following Dupilumab Treatment. The Journal of allergy and clinical immunology Simpson, E. L., Schlievert, P. M., Yoshida, T., Lussier, S., Boguniewicz, M., Hata, T., Fuxench, Z., De Benedetto, A., Ong, P. Y., Ko, J., Calatroni, A., Rudman Spergel, A. K., Plaut, M., Quataert, S. A., Kilgore, S. H., Peterson, L., Gill, A. L., David, G., Mosmann, T., Gill, S. R., Leung, D. Y., Beck, L. A. 2023


    Atopic dermatitis (AD) is an inflammatory disorder characterized by dominant type 2 inflammation leading to chronic pruritic skin lesions, allergic comorbidities and Staphylococcus aureus skin colonization and infections. S. aureus is thought to play a role in AD severity.We characterized the changes in the host-microbial interface in AD subjects following type 2 blockade with dupilumab.Participants (n=71) with moderate-severe AD were enrolled in a randomized (dupilumab vs placebo; 2:1), double-blind study at Atopic Dermatitis Research Network centers. Bioassays were performed at multiple timepoints: S. aureus and virulence factor quantification, 16s rRNA microbiome, serum biomarkers, skin transcriptomic analyses and peripheral blood T-cell phenotyping.At baseline, 100% of participants were S. aureus colonized on the skin surface. Dupilumab treatment resulted in significant reductions in S. aureus after only 3 days (compared to placebo); 11 days before clinical improvement. Participants with the greatest S. aureus reductions had the best clinical outcomes, and these reductions correlated with reductions in serum CCL17 and disease severity. Reductions (10-fold) in S. aureus cytotoxins (day 7), perturbations in Th17 subsets (day 14), and increased expression of genes relevant for IL-17, neutrophil and complement pathways (day 7) were also observed.Blockade of IL-4 and IL-13 signaling, very rapidly (day 3) reduces S. aureus abundance in AD subjects, and this reduction correlates with reductions in the type 2 biomarker, CCL17 and measures of AD severity (excluding itch). Immunoprofiling and/or transcriptomics suggest a role for Th17, neutrophils and complement activation as potential mechanisms to explain these findings.

    View details for DOI 10.1016/j.jaci.2023.05.026

    View details for PubMedID 37315812

  • Evolution of a Project to Improve Inpatient-to-Outpatient Dermatology Care Transitions: Mixed Methods Evaluation. JMIR dermatology Kling, S. M., Aleshin, M. A., Saliba-Gustafsson, E. A., Garvert, D. W., Brown-Johnson, C. G., Amano, A., Kwong, B. Y., Calugar, A., Shaw, J. G., Ko, J. M., Winget, M. 2023; 6: e43389


    BACKGROUND: In-hospital dermatological care has shifted from dedicated dermatology wards to consultation services, and some consulted patients may require postdischarge follow-up in outpatient dermatology. Safe and timely care transitions from inpatient-to-outpatient specialty care are critical for patient health, but communication around these transitions can be disjointed, and workflows can be complex.OBJECTIVE: In this 3-phase quality improvement effort, we developed and evaluated an intervention that leveraged an electronic health record (EHR) feature, known as SmartPhrase, to enable a new workflow to improve transitions from inpatient care to outpatient dermatology.METHODS: Phase 1 (February-March 2021) included interviews with patients and process mapping with key stakeholders to identify gaps and inform an intervention: a SmartPhrase table and associated workflow to promote collection of patient information needed for scheduling follow-up and closed-loop communication between dermatology and scheduling teams. In phase 2 (April-May 2021), semistructured interviews-with dermatologists (n=5), dermatology residents (n=5), and schedulers (n=6)-identified pain points and refinements. In phase 3, the intervention was evaluated by triangulating data from these interviews with measured changes in scheduling efficiency, visit completion, and messaging volume preimplementation (January-February 2021) and postimplementation (April-May 2021).RESULTS: Preintervention pain points included unclear workflow for care transitions, limited patient input in follow-up planning, multiple messaging channels (eg, EHR based, email, and phone messages), and time-inefficient patient tracking. The intervention addressed most pain points; interviewees reported the intervention was easy to adopt and improved scheduling efficiency, workload, and patient involvement. More visits were completed within the desired timeframe of 14 days after discharge during the postimplementation period (21/47, 45%) than the preimplementation period (28/41, 68%; P=.03). The messaging workload also decreased from 88 scheduling-related messages sent for 25 patients before implementation to 30 messages for 8 patients after implementation.CONCLUSIONS: Inpatient-to-outpatient specialty care transitions are complex and involve multiple stakeholders, thus requiring multifaceted solutions. With deliberate evaluation, broad stakeholder input, and iteration, we designed and implemented a successful solution using a standard EHR feature, SmartPhrase, integrated into a standardized workflow to improve the timeliness of posthospital specialty care and reduce workload.

    View details for DOI 10.2196/43389

    View details for PubMedID 37632927

  • Rapid reduction in S. aureus in atopic dermatitis subjects following dupilumab treatment Simpson, E., Schlievert, P., Yoshida, T., Lussier, S., Boguniewicz, M., Hata, T., Fuxench, Z., De Benedetto, A., Ong, P., Ko, J., Calatroni, A., Spergel, A., Plaut, M., Quataert, S., Kilgore, S., Peterson, L., Gill, A., David, G., Mosmann, T., Gill, S., Leung, D., Beck, L. ELSEVIER SCIENCE INC. 2023: S274
  • Optimizing Virtual Visits and Reducing Inbox Messages Using a Pre-Visit Questionnaire: A Quality Improvement Project. Journal of the American Academy of Dermatology Gomez, J., Mazzoleni, M., Calugar, A., Pol-Rodriguez, M., Ko, J. M., Bailey, E. E. 2023

    View details for DOI 10.1016/j.jaad.2022.12.045

    View details for PubMedID 36720367

  • Real-world post-deployment performance of a novel machine learning-based digital health technology for skin lesion assessment and suggestions for post-market surveillance. Frontiers in medicine Thomas, L., Hyde, C., Mullarkey, D., Greenhalgh, J., Kalsi, D., Ko, J. 2023; 10: 1264846


    Introduction: Deep Ensemble for Recognition of Malignancy (DERM) is an artificial intelligence as a medical device (AIaMD) tool for skin lesion assessment.Methods: We report prospective real-world performance from its deployment within skin cancer pathways at two National Health Service hospitals (UK) between July 2021 and October 2022.Results: A total of 14,500 cases were seen, including patients 18-100 years old with Fitzpatrick skin types I-VI represented. Based on 8,571 lesions assessed by DERM with confirmed outcomes, versions A and B demonstrated very high sensitivity for detecting melanoma (95.0-100.0%) or malignancy (96.0-100.0%). Benign lesion specificity was 40.7-49.4% (DERM-vA) and 70.1-73.4% (DERM-vB). DERM identified 15.0-31.0% of cases as eligible for discharge.Discussion: We show DERM performance in-line with sensitivity targets and pre-marketing authorisation research, and it reduced the caseload for hospital specialists in two pathways. Based on our experience we offer suggestions on key elements of post-market surveillance for AIaMDs.

    View details for DOI 10.3389/fmed.2023.1264846

    View details for PubMedID 38020164

  • A Multicenter Descriptive Analysis of 270 Men with Frontal Fibrosing Alopecia and Lichen Planopilaris in the United States. Journal of the American Academy of Dermatology Pathoulas, J. T., Flanagan, K. E., Walker, C. J., Collins, M. S., Ali, S., Pupo Wiss, I. M., Cotsarelis, G., Milbar, H., Huang, K., Mostaghimi, A., Scott, D., Han, J. J., Lee, K. J., Hordinsky, M. K., Farah, R. S., Bellefeuille, G., Raymond, O., Bergfeld, W., Ranasinghe, G., Shapiro, J., Lo Sicco, K. I., Gutierrez, D., Ko, J., Mirmirani, P., Mesinkovska, N., Yale, K. L., Goldberg, L. J., Tosti, A., Gwillim, E. C., Goh, C., Senna, M. M. 2022

    View details for DOI 10.1016/j.jaad.2022.10.060

    View details for PubMedID 36396001

  • A Cross-Sectional Analysis of The Environmental and Cost-Saving Benefits of Digital Dermatologic Care. Journal of the American Academy of Dermatology Gomez, J., Rizk, N., Linos, E., Ko, J. M., Bailey, E. E. 2022

    View details for DOI 10.1016/j.jaad.2022.08.033

    View details for PubMedID 36030984

  • Artificial Intelligence in the Detection of Skin Cancer. Journal of the American Academy of Dermatology Beltrami, E. J., Brown, A. C., Salmon, P. J., Leffell, D. J., Ko, J. M., Grant-Kels, J. M. 2022


    Recent advances in artificial intelligence (AI) in dermatology have demonstrated the potential to improve the accuracy of skin cancer detection. These capabilities may augment current diagnostic processes and improve the approach to management of skin cancer. To explain this technology, we discuss fundamental terminology, potential benefits, and limitations of AI and commercial applications relevant to dermatologists. A clearer understanding of the technology may help to reduce physician concerns about AI and promote its use in the clinical setting. Ultimately, the development and validation of AI technologies, their approval by regulatory agencies and widespread adoption by both dermatologists and other clinicians may enhance patient care. Technology-augmented detection of skin cancer has the potential to improve quality of life, reduce health care costs by reducing unnecessary procedures, and promote greater access to high quality skin assessment. Dermatologists play a critical role in the responsible development and deployment of AI capabilities applied to skin cancer.

    View details for DOI 10.1016/j.jaad.2022.08.028

    View details for PubMedID 35998842

  • Disparities in dermatology AI performance on a diverse, curated clinical image set. Science advances Daneshjou, R., Vodrahalli, K., Novoa, R. A., Jenkins, M., Liang, W., Rotemberg, V., Ko, J., Swetter, S. M., Bailey, E. E., Gevaert, O., Mukherjee, P., Phung, M., Yekrang, K., Fong, B., Sahasrabudhe, R., Allerup, J. A., Okata-Karigane, U., Zou, J., Chiou, A. S. 2022; 8 (32): eabq6147


    An estimated 3 billion people lack access to dermatological care globally. Artificial intelligence (AI) may aid in triaging skin diseases and identifying malignancies. However, most AI models have not been assessed on images of diverse skin tones or uncommon diseases. Thus, we created the Diverse Dermatology Images (DDI) dataset-the first publicly available, expertly curated, and pathologically confirmed image dataset with diverse skin tones. We show that state-of-the-art dermatology AI models exhibit substantial limitations on the DDI dataset, particularly on dark skin tones and uncommon diseases. We find that dermatologists, who often label AI datasets, also perform worse on images of dark skin tones and uncommon diseases. Fine-tuning AI models on the DDI images closes the performance gap between light and dark skin tones. These findings identify important weaknesses and biases in dermatology AI that should be addressed for reliable application to diverse patients and diseases.

    View details for DOI 10.1126/sciadv.abq6147

    View details for PubMedID 35960806

  • Teledermatology to Facilitate Patient Care Transitions From Inpatient to Outpatient Dermatology: Mixed Methods Evaluation. Journal of medical Internet research Kling, S. M., Saliba-Gustafsson, E. A., Winget, M., Aleshin, M. A., Garvert, D. W., Amano, A., Brown-Johnson, C. G., Kwong, B. Y., Calugar, A., El-Banna, G., Shaw, J. G., Asch, S. M., Ko, J. M. 2022; 24 (8): e38792


    BACKGROUND: Both clinicians and patients have increasingly turned to telemedicine to improve care access, even in physical examination-dependent specialties such as dermatology. However, little is known about whether teledermatology supports effective and timely transitions from inpatient to outpatient care, which is a common care coordination gap.OBJECTIVE: Using mixed methods, this study sought to retrospectively evaluate how teledermatology affected clinic capacity, scheduling efficiency, and timeliness of follow-up care for patients transitioning from inpatient to outpatient dermatology care.METHODS: Patient-level encounter scheduling data were used to compare the number and proportion of patients who were scheduled and received in-clinic or video dermatology follow-ups within 14 and 90 days after discharge across 3 phases: June to September 2019 (before teledermatology), June to September 2020 (early teledermatology), and February to May 2021 (sustained teledermatology). The time from discharge to scheduling and completion of patient follow-up visits for each care modality was also compared. Dermatology clinicians and schedulers were also interviewed between April and May 2021 to assess their perceptions of teledermatology for postdischarge patients.RESULTS: More patients completed follow-up within 90 days after discharge during early (n=101) and sustained (n=100) teledermatology use than at baseline (n=74). Thus, the clinic's capacity to provide follow-up to patients transitioning from inpatient increased from baseline by 36% in the early (101 from 74) and sustained (100 from 74) teledermatology periods. During early teledermatology use, 61.4% (62/101) of the follow-ups were conducted via video. This decreased significantly to 47% (47/100) in the following year, when COVID-19-related restrictions started to lift (P=.04), indicating more targeted but still substantial use. The proportion of patients who were followed up within the recommended 14 days after discharge did not differ significantly between video and in-clinic visits during the early (33/62, 53% vs 15/39, 38%; P=.15) or sustained (26/53, 60% vs 28/47, 49%; P=.29) teledermatology periods. Interviewees agreed that teledermatology would continue to be offered. Most considered postdischarge follow-up patients to be ideal candidates for teledermatology as they had undergone a recent in-person assessment and might have difficulty attending in-clinic visits because of competing health priorities. Some reported patients needing technological support. Ultimately, most agreed that the choice of follow-up care modality should be the patient's own.CONCLUSIONS: Teledermatology could be an important tool for maintaining accessible, flexible, and convenient care for recently discharged patients needing follow-up care. Teledermatology increased clinic capacity, even during the pandemic, although the timeliness of care transitions did not improve. Ultimately, the care modality should be determined through communication with patients to incorporate their and their caregivers' preferences.

    View details for DOI 10.2196/38792

    View details for PubMedID 35921146

  • Impact of the COVID-19 pandemic on dermatology visits among older adults and racial and ethnic minorities Rizk, N., Mathur, M. B., Ko, J., Chiou, A., Hunt, I., van Egmond, S., Linos, E. ELSEVIER SCIENCE INC. 2022: S65
  • Rapid reduction in S. aureus & cytotoxins in dupilumab treated atopic dermatitis subjects Beck, L. A., Boguniewicz, M., Hata, T., Fuxench, Z., Simpson, E., De Benedetto, A., Ko, J., Ong, P., Yoshida, T., Gallo, R., Lussier, S., David, G., Schlievert, P., Gill, S., Spergel, A., Leung, D. Y. ELSEVIER SCIENCE INC. 2022: S88
  • Development of a digital tool for home-based monitoring of skin disease for older adults van Egmond, S., Cai, Z., Nava, V., Rapaport, B., Ko, J., Chiou, A., Sarin, K. Y., Tang, J., Bousheri, S., Zhang, L., Linos, E. ELSEVIER SCIENCE INC. 2022: S59
  • Using qualitative methods to establish the clinically meaningful threshold for treatment success in alopecia areata. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation Wyrwich, K. W., Kitchen, H., Knight, S., Aldhouse, N. V., Macey, J., Mesinkovska, N., Ko, J. M., King, B. A. 2022


    PURPOSE: Traditionally, appropriate anchors are used to investigate the amount of change on a clinician-reported outcome assessment that is meaningful to individual patients. However, novel qualitative methods involving input from disease state experts together with patients may better inform the individual improvement threshold for demonstrating the clinical benefit of new treatments. This study aimed to establish a clinically meaningful threshold for treatment success for the clinician-reported Severity of Alopecia Tool (SALT) score for patients with alopecia areata (AA).METHODS: A purposive sample of 10 dermatologists expert in AA and 30 adult and adolescent patients with AA and a history of≥50% scalp hair loss were recruited. Semi-structured interview questions explored the outcome that represented treatment success to clinicians and patients. Findings were analyzed using thematic methods to identify treatment success thresholds.RESULTS: Both informant groups confirmed scalp hair amount as the outcome of priority. Most expert clinicians considered a static threshold of 80% (n=5) or 75% (n=3) of the scalp hair as a treatment success. Most patient responses ranged from 70 to 90% (median: 80% of the scalp hair). Subsequently, queried patients confirmed that achieving SALT score≤20 with treatment would be a success, as reflected in the Alopecia Areata Investigator Global Assessment (AA-IGA). The novel qualitative processes used to inform this meaningful threshold reflects a clinician-then-patient process for: (a) confirmation of the patient outcome of priority; and (b) clinician input on a preliminary treatment success level for independent understanding among patients.CONCLUSION: This qualitative investigation of expert clinicians-then-patients with AA confirmed that achieving an amount of 80% or more scalp hair (SALT score≤20) was an appropriate individual treatment success threshold indicating clinically meaningful improvement for patients with≥50% scalp hair loss. A qualitative investigation of a quantifiable treatment success threshold is possible through a well-designed interview process with expert clinicians and the appropriate patient population.

    View details for DOI 10.1007/s11136-022-03170-7

    View details for PubMedID 35821174

  • Defining Severity in Alopecia Areata: Current Perspectives and a Multidimensional Framework. Dermatology and therapy King, B. A., Senna, M. M., Ohyama, M., Tosti, A., Sinclair, R. D., Ball, S., Ko, J. M., Glashofer, M., Pirmez, R., Shapiro, J. 2022


    Alopecia areata (AA) is an autoimmune disease characterized by nonscarring hair loss. As a clinically heterogeneous disease, various classification systems have evolved for defining its severity. In this high-level review of the literature, we discuss the traditional classification systems for AA severity and their strengths and weaknesses. Most recent classifications have focused on the extent of scalp hair loss as a defining feature, but additional clinical aspects of the disease, including location, pattern, and duration of hair loss as well as impact on the patient's quality of life, are also relevant. These various components have typically been used unidimensionally to classify patients. We propose a multidimensional framework to define AA severity that incorporates multiple patient- and illness-related domains. Using such a framework, dermatologists may better assess the severity of the disease for the individual patient beyond the extent of hair loss.

    View details for DOI 10.1007/s13555-022-00711-3

    View details for PubMedID 35357658

  • Two Phase 3 Trials of Baricitinib for Alopecia Areata. The New England journal of medicine King, B., Ohyama, M., Kwon, O., Zlotogorski, A., Ko, J., Mesinkovska, N. A., Hordinsky, M., Dutronc, Y., Wu, W., McCollam, J., Chiasserini, C., Yu, G., Stanley, S., Holzwarth, K., DeLozier, A. M., Sinclair, R., BRAVE-AA Investigators 2022


    BACKGROUND: Alopecia areata is an autoimmune condition characterized by rapid hair loss in the scalp, eyebrows, and eyelashes, for which treatments are limited. Baricitinib, an oral, selective, reversible inhibitor of Janus kinases 1 and 2, may interrupt cytokine signaling implicated in the pathogenesis of alopecia areata.METHODS: We conducted two randomized, placebo-controlled, phase 3 trials (BRAVE-AA1 and BRAVE-AA2) involving adults with severe alopecia areata with a Severity of Alopecia Tool (SALT) score of 50 or higher (range, 0 [no scalp hair loss] to 100 [complete scalp hair loss]). Patients were randomly assigned in a 3:2:2 ratio to receive once-daily baricitinib at a dose of 4 mg, baricitinib at a dose of 2 mg, or placebo. The primary outcome was a SALT score of 20 or less at week 36.RESULTS: We enrolled 654 patients in the BRAVE-AA1 trial and 546 in the BRAVE-AA2 trial. The estimated percentage of patients with a SALT score of 20 or less at week 36 was 38.8% with 4-mg baricitinib, 22.8% with 2-mg baricitinib, and 6.2% with placebo in BRAVE-AA1 and 35.9%, 19.4%, and 3.3%, respectively, in BRAVE-AA2. In BRAVE-AA1, the difference between 4-mg baricitinib and placebo was 32.6 percentage points (95% confidence interval [CI], 25.6 to 39.5), and the difference between 2-mg baricitinib and placebo was 16.6 percentage points (95% CI, 9.5 to 23.8) (P<0.001 for each dose vs. placebo). In BRAVE-AA2, the corresponding values were 32.6 percentage points (95% CI, 25.6 to 39.6) and 16.1 percentage points (95% CI, 9.1 to 23.2) (P<0.001 for each dose vs. placebo). Secondary outcomes for baricitinib at a dose of 4 mg but not at a dose of 2 mg generally favored baricitinib over placebo. Acne, elevated levels of creatine kinase, and increased levels of low- and high-density lipoprotein cholesterol were more common with baricitinib than with placebo.CONCLUSIONS: In two phase 3 trials involving patients with severe alopecia areata, oral baricitinib was superior to placebo with respect to hair regrowth at 36 weeks. Longer trials are required to assess the efficacy and safety of baricitinib for alopecia areata. (Funded by Eli Lilly under license from Incyte; BRAVE-AA1 and BRAVE-AA2 numbers, NCT03570749 and NCT03899259.).

    View details for DOI 10.1056/NEJMoa2110343

    View details for PubMedID 35334197

  • Predictors of Quality of Life in Patients with Alopecia Areata. The Journal of investigative dermatology Senna, M., Ko, J., Glashofer, M., Walker, C., Ball, S., Heredia, E. E., Zhu, B., Shapiro, J. 2022


    Although alopecia areata (AA) severity is often defined by the degree of scalp hair loss, its impact on quality of life (QoL) can also be a defining measure of severity. In this cross-sectional study (AA Disease Specific Program [AA DSP]), 259 patients were surveyed for demographics, AA illness characteristics, QoL (Skindex-16 AA), and daily impairment (Work Productivity Activity Impairment [WPAI]). The association between patient demographic and illness variables, the Skindex-16 AA scores, and the WPAI scores were analyzed using regression analyses. Mean age of patients was 39 years (51% female). Self-reported severity of current AA was rated as mild (21%), moderate (54%), and severe (25%). Highest impairment was observed for the Skindex-16 AA Emotions and the WPAI daily activity performance scores. Although the degree of scalp hair loss (Physician Severity of Alopecia Tool [SALT] score) was not predictive of QoL, patients' self-report of moderate or severe disease, gender (females more impacted), and eyebrow and eyelash involvement were consistently, and incrementally, predictors of diminished QoL. The present results suggest patient's perception of severity as well as the presence of eyelash and eyebrow hair loss are also impactful and should be considered in defining severity of disease.

    View details for DOI 10.1016/j.jid.2022.02.019

    View details for PubMedID 35331716

  • Effect of Dupilumab on the Host-Microbe Interface in Atopic Dermatitis Beck, L., Boguniewicz, M., Hata, T., Fuxench, Z., Simpson, E., De Benedetto, A., Ko, J., Ong, P., Yoshida, T., Gallo, R., Brar, K., Schlievert, P., Villareal, M., Lussier, S., David, G., Rudman-Spergel, A., Leung, D. MOSBY-ELSEVIER. 2022: AB150
  • Partnering with a senior living community to optimize teledermatology via full body skin screening during the COVID-19 pandemic: a pilot program Skin Health and Disease Trinh, P., Yekrang, K., Phung, M., Pugliese, S., Chang, A., Bailey, E., Ko, J., Sarin, K. 2022

    View details for DOI 10.1002/ski2.141

  • It's all alopecia areata - it's time to abandon the terms alopecia totalis and alopecia universalis. Journal of the American Academy of Dermatology Peterson, D., Craiglow, B. G., Mesinkovska, N. A., Ko, J., Senna, M. M., King, B. 2021

    View details for DOI 10.1016/j.jaad.2021.09.056

    View details for PubMedID 34678234

  • Efficacy and safety of baricitinib in adults with Alopecia Areata: Phase 3 results from a randomized controlled trial (BRAVE-AA1) King, B., Kwon, O., Mesinkovska, N., Ko, J., Dutronc, Y., Wu, W., McCollam, J., Yu, G., Holzwarth, K., DeLozier, A. M., Hordinsky, M. ELSEVIER SCIENCE INC. 2021: B18
  • Response to baricitinib in the treatment of patients with early and late onset alopecia areata in the phase 2 portion of BRAVE-AA1 randomized controlled trial Ko, J., Roberts, J., Hordinsky, M., Taylor, S., Mostaghimi, A., Chiasserini, C., Ding, Y., Chen, Y., Wu, W., Dutronc, Y. MOSBY-ELSEVIER. 2021: AB155
  • Foundational Considerations for Artificial Intelligence Utilizing Ophthalmic Images. Ophthalmology Abramoff, M. D., Cunningham, B., Patel, B., Eydelman, M. B., Leng, T., Sakamoto, T., Blodi, B., Grenon, S. M., Wolf, R. M., Manrai, A. K., Ko, J. M., Chiang, M. F., Char, D., Collaborative Community on Ophthalmic Imaging Executive Committee and Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group 2021


    IMPORTANCE: The development of Artificial Intelligence (AI) and other machine diagnostic systems, also known as Software as a Medical Device (SaMD), and its recent introduction into clinical practice, requires a deeply-rooted foundation in bioethics, for consideration by regulatory agencies and other stakeholders around the globe.OBJECTIVES: Initiate a dialogue on the issues to consider when developing a bioethically sound foundation for AI in medicine, based on images of eye structures, for discussion with all stakeholders.EVIDENCE REVIEW: The scope of the issues and summaries of the discussions under consideration by the Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group, as first presented during the Collaborative Community on Ophthalmic Imaging inaugural meeting on September 7, 2020, and afterwards in the working group.FINDINGS: AI has the potential to fundamentally improve the access to healthcare and patient outcomes, while decreasing disparities, lowering cost, as well as enhancing the care team. Nevertheless, substantial concerns exist. Ethicists, AI algorithm experts, as well as the Food and Drug Administration (FDA) and other regulatory agencies, industry, patient advocacy groups, clinicians and their professional societies, other provider groups, payors, and other healthcare stakeholders working together in collaborative communities to resolve such issues as non-maleficence, autonomy and equity, is essential to attain this potential, and impacts all levels of the design, validation and implementation of AI in medicine. Design, validation and implementation of AI warrant meticulous attention.CONCLUSIONS AND RELEVANCE: The development of a bioethically sound foundation may be possible if it is based in non-maleficence, autonomy and equity, for considerations for the design, validation and implementation for AI systems. Achieving such a foundation will be helpful for continuing successful introduction into medicine, before consideration by regulatory agencies around the globe.Fundamental improvements in accessibility and quality of healthcare, decrease in health disparities, and lower cost can thereby be achieved. These considerations should be discussed with all stakeholders and expanded upon as a useful initiation of this dialogue.

    View details for DOI 10.1016/j.ophtha.2021.08.023

    View details for PubMedID 34478784

  • Development of the Alopecia Areata Scale for Clinical Use: Results of an Academic-Industry Collaborative Effort. Journal of the American Academy of Dermatology King, B. A., Mesinkovska, N. A., Craiglow, B., Kindred, C., Ko, J., McMichael, A., Shapiro, J., Goh, C., Mirmirani, P., Tosti, A., Hordinsky, M., Huang, K. P., Castelo-Soccio, L., Bergfeld, W., Paller, A. S., Mackay-Wiggan, J., Glashofer, M., Aguh, C., Piliang, M., Yazdan, P., Lo Sicco, K., Cassella, J. V., Koenigsberg, J., Ahluwalia, G., Ghorayeb, E., Fakharzadeh, S., Napatalung, L., Gandhi, K., DeLozier, A. M., Nunes, F. P., Senna, M. M. 2021


    BACKGROUND: The current classification for Alopecia Areata (AA) does not provide a consistent assessment of disease severity.OBJECTIVE: To develop an AA severity scale based on expert experience.METHODS: A Modified Delphi process was utilized. An advisory group of 22 US-based AA clinical experts was formed to develop this AA scale. Representatives from the pharmaceutical industry provided feedback during its development.RESULTS: Survey responses were used to draft severity criteria, aspiring to develop a simple scale that may be easily applied in clinical practice. A consensus vote was held to determine the final AA severity statement, with all AA experts agreeing with the adoption of the proposed scale.LIMITATIONS: The scale is a static assessment intended to be used in clinical practice, and not clinical trials.CONCLUSIONS: The final AA disease severity scale, anchored on extent of hair loss, captures key features commonly used by AA experts in clinical practice. This scale will better aid clinicians in appropriately assessing severity in patients with this common disease.

    View details for DOI 10.1016/j.jaad.2021.08.043

    View details for PubMedID 34474079

  • Alopecia Areata Treatment Patterns, Healthcare Resource Utilization, and Comorbidities in the US Population Using Insurance Claims. Advances in therapy Senna, M., Ko, J., Tosti, A., Edson-Heredia, E., Fenske, D. C., Ellinwood, A. K., Rueda, M. J., Zhu, B., King, B. 2021


    INTRODUCTION: Alopecia areata (AA) is an autoimmune disorder causing sudden, non-scarring hair loss. There are currently no drugs approved for AA treatment. This study assessed prevalence of comorbidities, treatments, and healthcare costs and resource utilization among patients with AA in the USA.METHODS: Patients diagnosed with AA between January 2011 and December 2018 were identified in IBM MarketScan Research Databases. Eligible patients had no other hair loss-related disorders and were continuously enrolled with medical and pharmacy benefits at least 12months before and after AA diagnosis. Descriptive statistics were used to summarize comorbid conditions, treatments related to AA or other autoimmune/inflammatory conditions, and all-cause and AA-specific healthcare costs and resource utilization identified from claims data.RESULTS: A total of 68,121 patients with AA were identified. Mean (SD) age was 40.3 (17.8) years and 61.0% were female. The most common comorbidities included hyperlipidemia (22.4%), hypertension (21.8%), thyroid disorders (13.1%), contact dermatitis or eczema (10.8%), depression (9.5%), and anxiety (8.4%). Comorbid autoimmune diseases included atopic dermatitis (2.8%), psoriasis (2.1%), chronic urticaria (1.5%), and rheumatoid arthritis (1.1%). During the 12-month follow-up period, 37,995 patients (55.8%) were prescribed treatment for their AA or other comorbid autoimmune/inflammatory disease; 44.9% of treated patients were prescribed therapy within 7days of AA diagnosis. Of patients receiving treatment, 80.3% received topical steroids and 30.0% received oral steroids. Mean (SD) total healthcare costs were $11,241.21 ($43,839.69) for all-causes and $419.12 ($1534.99) for AA. AA-related expenses were driven by outpatient and prescription costs.CONCLUSION: Patients with AA have a high comorbidity burden and lack of treatment. Current AA treatments, including systemic therapies other than oral steroids, were not frequently utilized in this study population. Healthcare costs incurred by patients with AA went beyond AA-related expenses. Longitudinal data are needed to better understand treatment trajectories and the disease burden in patients with AA.

    View details for DOI 10.1007/s12325-021-01845-0

    View details for PubMedID 34292518

  • Association of Resilience and Perceived Stress in Patients with Alopecia Areata: A Cross Sectional Study. Journal of the American Academy of Dermatology Han, J. J., Li, S. J., Joyce, C. J., Burns, L. J., Yekrang, K., Senna, M. M., Ko, J. M., Huang, K. P., Mostaghimi, A. 2021

    View details for DOI 10.1016/j.jaad.2021.06.879

    View details for PubMedID 34252468

  • Efficacy and safety of the oral Janus kinase inhibitor baricitinib in the treatment of adults with alopecia areata: Phase 2 results from a randomized controlled study. Journal of the American Academy of Dermatology King, B., Ko, J., Forman, S., Ohyama, M., Mesinkovska, N., Yu, G., McCollam, J., Gamalo, M., Janes, J., Edson-Heredia, E., Holzwarth, K., Dutronc, Y. 2021


    BACKGROUND: There are no Food and Drug Administration-approved treatments for alopecia areata (AA).OBJECTIVE: To evaluate efficacy and safety of baricitinib in patients with ≥50% scalp hair loss in a Phase 2 study of adults with AA (BRAVE-AA1; NCT03570749).METHODS: Patients were randomized 1:1:1:1 to receive placebo, baricitinib 1-mg, 2-mg, or 4-mg once daily. Two consecutive interim analyses were performed after all patients completed Weeks 12 and 36 or had discontinued treatment prior to these time points. The primary endpoint was the proportion of patients achieving Severity of Alopecia Tool (SALT) score ≤20 at Week 36. Logistic regression was used with nonresponder imputation for missing data.RESULTS: A total of 110 patients were randomized (placebo: 28; baricitinib 1-mg: 28; 2-mg: 27; 4-mg: 27). Baricitinib 1-mg dose was dropped after the first interim analysis based on lower SALT30 response rate. At Week 36, the proportion of patients achieving SALT score ≤20 was significantly greater in baricitinib 2-mg (33.3%, p=0.016) and 4-mg (51.9%, p=0.001) groups versus placebo (3.6%). Baricitinib was well tolerated with no new safety findings.LIMITATIONS: Small sample size limits generalizability of results.CONCLUSION: These results support the efficacy and safety of baricitinib in patients with ≥50% scalp hair loss.

    View details for DOI 10.1016/j.jaad.2021.05.050

    View details for PubMedID 34090959

  • Dermatologists' Perspectives on Artificial Intelligence and Augmented Intelligence-A Cross-sectional Survey. JAMA dermatology Nelson, C. A., Pachauri, S., Balk, R., Miller, J., Theunis, R., Ko, J. M., Kovarik, C. L. 2021

    View details for DOI 10.1001/jamadermatol.2021.1685

    View details for PubMedID 34037674

  • Effect of dupilumab on the host-microbe interface in atopic dermatitis Beck, L. A., Boguniewicz, M., Hatta, T., Fuxench, Z., Simpson, E., De Benedetto, A., Ko, J., Ong, P., Yoshida, T., Gallo, R., Schlievert, P., Gill, S., Mosmann, T., Berdyshev, E., David, G., Lussier, S., Spergel, A., Leung, D. ELSEVIER SCIENCE INC. 2021: S116
  • Clinical translation of choline and geranic acid deep eutectic solvent. Bioengineering & translational medicine Ko, J., Mandal, A., Dhawan, S., Shevachman, M., Mitragotri, S., Joshi, N. 2021; 6 (2): e10191


    Choline geranate deep eutectic solvent/ionic liquid (CAGE) has shown several desirable therapeutic properties including antimicrobial activity and ability to deliver drugs transdermally in research laboratories. Here, we describe the first report of clinical translation of CAGE from the lab into the clinic for the treatment of rosacea, a common chronic inflammatory skin disorder that affects the face. We describe the seven steps of clinical translation including (a) scale-up, (b) characterization, (c) stability analysis, (d) mechanism of action, (e) dose determination, (f) GLP toxicity study, and (g) human clinical study. We describe the challenges and outcomes in these steps, especially those that uniquely arise from the deep eutectic nature of CAGE. Our translational efforts led to a 12-week open-label phase 1b cosmetic study with CAGE1:2 gel (CGB400) in mild-moderate facial rosacea in 26 patients where CGB400 exhibited a marked reduction in the number of inflammatory lesions. These results demonstrate the therapeutic potential of CGB400 for treating rosacea as well as it provides insights into the translational journey of deep eutectic solvents, in particular CAGE, for dermatological applications.

    View details for DOI 10.1002/btm2.10191

    View details for PubMedID 34027084

    View details for PubMedCentralID PMC8126811

  • Dermatology consent form readability: A barrier to comprehension and inclusivity Faletsky, A., Han, J. J., Li, S. J., Lee, K., Soliman, Y., Stephens, M., Ko, J., Mostaghimi, A. ELSEVIER SCIENCE INC. 2021: S69
  • Raising the bar for Randomized Trials involving Artificial Intelligence: The SPIRIT-AI and CONSORT-AI Guidelines. The Journal of investigative dermatology Taylor, M., Liu, X., Denniston, A., Esteva, A., Ko, J., Daneshjou, R., Chan, A., SPIRIT-AI and CONSORT-AI Working Group 2021


    Artificial intelligence (AI)-based applications have the potential to improve the quality and efficiency of patient care in dermatology. Unique challenges in the development and validation of these technologies may limit their generalizability and real-world applicability. Before widespread adoption of AI interventions, randomized trials should be conducted to evaluate their efficacy, safety, and cost effectiveness in clinical settings. The recent SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - AI extension) and CONSORT-AI (Consolidated Standards of Reporting Trials - AI extension) guidance provide recommendations for reporting the methods and results of trials involving AI interventions. High-quality trials will provide gold standard evidence to support the adoption of AI for the benefit of patient care.

    View details for DOI 10.1016/j.jid.2021.02.744

    View details for PubMedID 33766511

  • Development and validation of the Brigham Eyelash Tool for Alopecia (BELA): A measure of eyelash alopecia areata. Journal of the American Academy of Dermatology Manjaly, P., Li, S. J., Tkachenko, E., Ko, J. M., Liu, K. J., Scott, D. A., Senna, M. M., Joyce, C. J., Mostaghimi, A., Huang, K. P. 2021

    View details for DOI 10.1016/j.jaad.2020.06.1034

    View details for PubMedID 33741177

  • Management of a Child vs an Adult Presenting With Acral Lesions During the COVID-19 Pandemic: A Practical Review CUTIS Clawson, R., Tabata, M. M., Ko, J. M. 2021; 107 (3): 139–42


    During the coronavirus disease 2019 (COVID-19) pandemic, there has been a rise in the diagnosis of acral lesions, including chilblains-like lesions, ischemia, and retiform purpura. Understanding the differences in presentation and severity of illness between children and adult patients is important for physicians to understand risk stratification and management of these lesions. We reviewed the literature on the acral lesions seen in children and adults with COVID-19 infection to offer guidelines for diagnosis and treatment.

    View details for DOI 10.12788/cutis.0203

    View details for Web of Science ID 000641142300020

    View details for PubMedID 33956605

  • TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Vodrahalli, K., Daneshjou, R., Novoa, R. A., Chiou, A., Ko, J. M., Zou, J. 2021; 26: 220–31


    Telehealth is an increasingly critical component of the health care ecosystem, especially due to the COVID-19 pandemic. Rapid adoption of telehealth has exposed limitations in the existing infrastructure. In this paper, we study and highlight photo quality as a major challenge in the telehealth workflow. We focus on teledermatology, where photo quality is particularly important; the framework proposed here can be generalized to other health domains. For telemedicine, dermatologists request that patients submit images of their lesions for assessment. However, these images are often of insufficient quality to make a clinical diagnosis since patients do not have experience taking clinical photos. A clinician has to manually triage poor quality images and request new images to be submitted, leading to wasted time for both the clinician and the patient. We propose an automated image assessment machine learning pipeline, TrueImage, to detect poor quality dermatology photos and to guide patients in taking better photos. Our experiments indicate that TrueImage can reject ~50% of the sub-par quality images, while retaining ~80% of good quality images patients send in, despite heterogeneity and limitations in the training data. These promising results suggest that our solution is feasible and can improve the quality of teledermatology care.

    View details for PubMedID 33691019

  • Prevalence of Potentially Allergenic Ingredients in Products Labeled for Eczema Care. Journal of the American Academy of Dermatology Schwartz, B. L., Honari, G., Chiou, A. S., Ko, J., Sarin, K. Y., Chen, J. K. 2021

    View details for DOI 10.1016/j.jaad.2021.05.038

    View details for PubMedID 34058279

  • TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos Vodrahalli, K., Daneshjou, R., Novoa, R. A., Chiou, A., Ko, J. M., Zou, J., Altman, R. B., Dunker, A. K., Hunter, L., Ritchie, M. D., Murray, T., Klein, T. E. WORLD SCIENTIFIC PUBL CO PTE LTD. 2021: 220-231
  • Dermatology consent form readability as a barrier to comprehension and inclusivity: a cross-sectional study. Journal of the American Academy of Dermatology Faletsky, A., Han, J. J., Soliman, Y., Stephens, M., Li, S., Lee, K. J., Ko, J., Mostaghimi, A. 2021

    View details for DOI 10.1016/j.jaad.2021.09.035

    View details for PubMedID 34582839

  • Bridging to a selective Janus kinase 1 inhibitor in severe atopic dermatitis: An instructive case with upadacitinib. JAAD case reports Nguyen, J., Chen, J. K., Honari, G., Pol-Rodriguez, M., Ko, J. M., Chiou, A. S. 2021; 7: 65–67

    View details for DOI 10.1016/j.jdcr.2020.10.023

    View details for PubMedID 33354610

  • Brigham Eyebrow Tool for Alopecia: A Reliable Assessment of Eyebrow Alopecia Areata. The journal of investigative dermatology. Symposium proceedings Tkachenko, E., Huang, K. P., Ko, J. M., Liu, K. J., Scott, D. A., Senna, M. M., Li, S. J., Joyce, C. J., Mostaghimi, A. 2020; 20 (1): S41–S44


    There are no tools to evaluate eyebrow involvement in patients with alopecia areata. We developed and assessed the reliability of the Brigham Eyebrow Tool for Alopecia (BETA) as a quantitative evaluation of eyebrow alopecia areata. BETA uses facial landmarks of eyebrow anatomy and is calculated using surface area and density. A total of 50 eyebrow images with varying levels of hair loss were distributed to six board-certified dermatologists at three academic medical centers with standardized instructions and examples. Interrater and intrarater reliability were calculated using intraclass correlation coefficients (ICCs). BETA demonstrated high interrater (ICC= 0.88, confidence interval= 0.83-0.92 right eyebrow scores and ICC= 0.90, confidence interval= 0.85-0.94 left eyebrow scores) and intrarater (ICC= 0.90, confidence interval= 0.85-0.93 right eyebrow scores and ICC= 0.91, confidence interval= 0.87-0.94 left eyebrow scores) reliability. When measured in the same patient with varying degrees of hair loss over time, BETA demonstrated sensitivity to change. BETA is a simple and reliable objective assessment of eyebrow alopecia areata. BETA is easy-to-use and quick to calculate, making it feasible for a variety of clinical and research settings. Although developed for alopecia areata, we hope that BETA will be investigated in other etiologies of eyebrow alopecia to serve as a universal tool for monitoring disease progression, improvement, and response to treatment.

    View details for DOI 10.1016/j.jisp.2020.06.001

    View details for PubMedID 33099383

  • Eyebrows Are Important in the Treatment of Alopecia Areata. The journal of investigative dermatology. Symposium proceedings Liu, L. Y., King, B. A., Ko, J. M. 2020; 20 (1): S37–S40


    Alopecia areata affects not only scalp hair but also other sites of body hair, including eyebrows. Our objective was to investigate the importance of eyebrows in the treatment goals of patients with alopecia areata. Through an online questionnaire, subjects were asked to assess satisfaction with the visually depicted level of response to treatment, using edited photographs depicting a range of eyebrows and scalp hair growth. The questionnaire was completed by 1,741 adults. Absent or partial growth of eyebrows and scalp hair elicited <25% satisfaction. Images depicting either complete eyebrows or complete scalp hair achieved satisfaction in >50% of participants. More participants were satisfied with complete eyebrows and no scalp hair (69%) than complete eyebrows and partial scalp hair (51%). Only when both eyebrows and scalp hair were completely regrown did extreme satisfaction levels reach 90.4%. Limitations include the online nature of the survey, lack of control group, and self-reported severity of alopecia areata in participants. These results suggest that eyebrows may be as important as scalp hair for patients assessing theoretical responses to treatment for alopecia areata. Future clinical studies should consider growth of eyebrows as an outcome measure on par with scalp hair growth.

    View details for DOI 10.1016/j.jisp.2020.04.006

    View details for PubMedID 33099382

  • Burden of Illness in Alopecia Areata: A Cross-Sectional Online Survey Study. The journal of investigative dermatology. Symposium proceedings Mesinkovska, N., King, B., Mirmirani, P., Ko, J., Cassella, J. 2020; 20 (1): S62–S68


    Previous QOL and disease burden studies have not captured all relevant aspects of living with alopecia areata (AA). To better understand the burden and everyday experience of living with moderate-to-severe AA, a cross-sectional, online, quantitative-qualitative survey was developed to assess symptoms, relationships, productivity, treatments, and financial burden. Adult patients were recruited from the National Alopecia Areata Foundation database. Data were analyzed descriptively. A total of 216 patients completed the survey. Most were female (83%), aged ≥45 years (59%), and white (78%). Nearly 2 of 3 respondents (62%) made different major life decisions (regarding relationships, education, or career) owing to AA. Most respondents (85%) stated coping with AA as a daily challenge, citing mental health issues, concealing hair loss, and others' reactions; 47% reported anxiety and/or depression. Many patients (75%) persistently concealed hair loss (mean time spent, 10.3 h/wk). Treatment discontinuation was common owing to lack of efficacy, side effects, and cost. Associated expenditures included buying wigs or hairpieces and psychotherapy (mean $2,000/y each). Survey respondents comprised a self-selected sample, which may not reflect the entire population. The impact of AA extends beyond cosmetic concerns and carries a considerable psychosocial burden. Efficacious, less burdensome AA treatments are needed to regrow hair and alleviate psychosocial sequelae.

    View details for DOI 10.1016/j.jisp.2020.05.007

    View details for PubMedID 33099390

  • The Role of Patients in Alopecia Areata Endpoint Development: Understanding Physical Signs and Symptoms. The journal of investigative dermatology. Symposium proceedings Wyrwich, K. W., Kitchen, H., Knight, S., Aldhouse, N. V., Macey, J., Nunes, F., Dutronc, Y., Mesinkovska, N. A., Ko, J. M., King, B. A. 2020; 20 (1): S71–S77


    Meaningful patient input to understand disease experience and patient expectations for improvement with treatment is essential for the selection and development of outcome measures for alopecia areata (AA) clinical trials. This study explored the physical signs and symptoms of AA through 30 semistructured interviews with adult (n= 25) and adolescent (n= 5) patients experienced with severe or very severe AA. Scalp hair loss was overwhelmingly the most important sign and symptom of AA. Nearly all patients (90%) considered scalp hair loss in their top three most bothersome physical signs and symptoms of AA, with 77% (n= 23) naming scalp hair loss as the most bothersome symptom. Other identified signs and symptoms in the top three most bothersome included eyebrow, eyelash, nose, body, and facial hair loss, as well as eye irritation and nail damage and/or appearance. Eyebrow (16%, n= 4), eyelash (4%, n= 1), nasal (4%, n= 1), and body (4%, n= 1) hair loss were identified by seven adult patients as the most bothersome signs and symptoms of AA. Conceptual saturation confirmed that a comprehensive understanding of this patient population's physical AA-related signs and symptoms was obtained. These findings indicate that the primary objective for new AA treatments for this patient population should be meaningful improvement in scalp hair growth to address the most troubling unmet need.

    View details for DOI 10.1016/j.jisp.2020.05.006

    View details for PubMedID 33099392

  • Clinical translation of choline and geranic acid deep eutectic solvent BIOENGINEERING & TRANSLATIONAL MEDICINE Ko, J., Mandal, A., Dhawan, S., Shevachman, M., Mitragotri, S., Joshi, N. 2020

    View details for DOI 10.1002/btm2.10191

    View details for Web of Science ID 000585792300001

  • Pernio-like eruption associated with COVID-19 in skin of color. JAAD case reports Daneshjou, R., Rana, J., Dickman, M., Yost, J. M., Chiou, A., Ko, J. 2020; 6 (9): 892–97

    View details for DOI 10.1016/j.jdcr.2020.07.009

    View details for PubMedID 32835046

  • Innovation interest within dermatology: a needs assessment for novel thought processes. Archives of dermatological research Lee, K. C., Lee, I., Okhovat, J., Ko, J., Powers, J. G., Ellis, D. L., Cheeley, J., Garibyan, L. 2020


    Medical innovation is crucial to advancing our field, and physicians have the potential to play a leading role due to their daily patient care experiences. The objective of this study was to evaluate the interest in, and barriers to participating in innovation. Two surveys were conducted; the first cross-sectional survey was conducted among attendees of the Advancing Innovation in Dermatology Forum in Feburary 2019. The second survey was conducted among trainees (resident/fellows) and faculty dermatologists at Brown, Emory, Iowa, Stanford, and Vanderbilt Universities between June and November 2019. Demographic data were collected, as well as factors involved with identifying problems, developing solutions, training in innovation, and perceived barriers to innovation. In the first survey, the greatest perceived benefits include bringing joy to one's work and increasing professional fulfillment with work. Innovation was also perceived to decrease burnout. In the second survey of academic centers, faculty more commonly expressed interest in identifying problems (p =0.04), and was also more confident in their ability to generate solutions to these problems as compared to trainees (p<0.01). Major barriers to participating in innovation processes included lack of time and lack of training or education in innovation. Both trainees and faculty groups noted a lack of knowledge in creating prototypes, understanding regulatory approval for medical products, and inexperience with pitching to investors or obtaining funding. Thesecross-sectional needs assessment surveys found a strong interest in innovation coupled with a lack of education in innovation processes. These findings suggest an urgent need and opportunity for providing formal training to empower dermatologists with the tools to lead innovation within our field.

    View details for DOI 10.1007/s00403-020-02118-6

    View details for PubMedID 32772260

  • Patient Perspectives on the Use of Artificial Intelligence for Skin Cancer Screening: A Qualitative Study. JAMA dermatology Nelson, C. A., Perez-Chada, L. M., Creadore, A., Li, S. J., Lo, K., Manjaly, P., Pournamdari, A. B., Tkachenko, E., Barbieri, J. S., Ko, J. M., Menon, A. V., Hartman, R. I., Mostaghimi, A. 2020


    Importance: The use of artificial intelligence (AI) is expanding throughout the field of medicine. In dermatology, researchers are evaluating the potential for direct-to-patient and clinician decision-support AI tools to classify skin lesions. Although AI is poised to change how patients engage in health care, patient perspectives remain poorly understood.Objective: To explore how patients conceptualize AI and perceive the use of AI for skin cancer screening.Design, Setting, and Participants: A qualitative study using a grounded theory approach to semistructured interview analysis was conducted in general dermatology clinics at the Brigham and Women's Hospital and melanoma clinics at the Dana-Farber Cancer Institute. Forty-eight patients were enrolled. Each interview was independently coded by 2 researchers with interrater reliability measurement; reconciled codes were used to assess code frequency. The study was conducted from May 6 to July 8, 2019.Main Outcomes and Measures: Artificial intelligence concept, perceived benefits and risks of AI, strengths and weaknesses of AI, AI implementation, response to conflict between human and AI clinical decision-making, and recommendation for or against AI.Results: Of 48 patients enrolled, 26 participants (54%) were women; mean (SD) age was 53.3 (21.7) years. Sixteen patients (33%) had a history of melanoma, 16 patients (33%) had a history of nonmelanoma skin cancer only, and 16 patients (33%) had no history of skin cancer. Twenty-four patients were interviewed about a direct-to-patient AI tool and 24 patients were interviewed about a clinician decision-support AI tool. Interrater reliability ratings for the 2 coding teams were kappa=0.94 and kappa=0.89. Patients primarily conceptualized AI in terms of cognition. Increased diagnostic speed (29 participants [60%]) and health care access (29 [60%]) were the most commonly perceived benefits of AI for skin cancer screening; increased patient anxiety was the most commonly perceived risk (19 [40%]). Patients perceived both more accurate diagnosis (33 [69%]) and less accurate diagnosis (41 [85%]) to be the greatest strength and weakness of AI, respectively. The dominant theme that emerged was the importance of symbiosis between humans and AI (45 [94%]). Seeking biopsy was the most common response to conflict between human and AI clinical decision-making (32 [67%]). Overall, 36 patients (75%) would recommend AI to family members and friends.Conclusions and Relevance: In this qualitative study, patients appeared to be receptive to the use of AI for skin cancer screening if implemented in a manner that preserves the integrity of the human physician-patient relationship.

    View details for DOI 10.1001/jamadermatol.2019.5014

    View details for PubMedID 32159733

  • Dupilumab Treatment of Nummular Dermatitis: A Retrospective Cohort Study. Journal of the American Academy of Dermatology Choi, S. n., Zhu, G. A., Lewis, M. A., Honari, G. n., Chiou, A. S., Ko, J. n., Chen, J. K. 2020

    View details for DOI 10.1016/j.jaad.2019.12.054

    View details for PubMedID 31923445

  • Dupilumab for occupational irritant hand dermatitis in a nonatopic individual: A case report. JAAD case reports Zhu, G. A., Honari, G. n., Ko, J. M., Chiou, A. S., Chen, J. K. 2020; 6 (4): 296–98

    View details for DOI 10.1016/j.jdcr.2020.02.010

    View details for PubMedID 32258302

    View details for PubMedCentralID PMC7109358

  • The Alopecia Areata Investigator Global Assessment scale: a measure for evaluating clinically meaningful success in clinical trials. The British journal of dermatology Wyrwich, K. W., Kitchen, H. n., Knight, S. n., Aldhouse, N. V., Macey, J. n., Nunes, F. P., Dutronc, Y. n., Mesinkovska, N. n., Ko, J. M., King, B. A. 2020


    Content-valid and clinically meaningful instruments are required to evaluate outcomes of therapeutic interventions in alopecia areata (AA).To develop an Investigator's Global Assessment (IGA) to interpret treatment response in AA treatment studies.Qualitative interviews were conducted in the USA with expert dermatologists and with patients with AA who had experienced ≥ 50% scalp-hair loss. Thematic data analysis identified critical outcomes and evaluated the content validity of the new IGA.Expert clinicians (n = 10) judged AA treatment success by the amount of scalp-hair growth (median 80% scalp hair). Adult (n = 25) and adolescent (n = 5) patients participated. Scalp-hair loss was the most bothersome AA sign/symptom for most patients. Perceived treatment success - short of 100% scalp hair - was the presence of ~ 70-90% scalp hair (median 80%). Using additional clinician and patient insights, the Alopecia Areata Investigator Global Assessment (AA-IGA™) was developed. This clinician-reported outcome assessment is an ordinal, static measure comprising five severity categories of scalp-hair loss. Nearly all clinicians and patients in this study agreed that, for patients with ≥ 50% scalp-hair loss, successful treatment would be hair regrowth resulting in ≤ 20% scalp-hair loss.We recommend using the Severity of Alopecia Tool to assess the extent (0-100%) of scalp-hair loss. The AA-IGA is a robust ordinal measure providing distinct and clinically meaningful gradations of scalp-hair loss that reflects patients' and expert clinicians' perspectives and treatment expectations.

    View details for DOI 10.1111/bjd.18883

    View details for PubMedID 31970750

  • Development of the Scalp Hair Assessment PRO™ measure for alopecia areata. The British journal of dermatology Wyrwich, K. W., Kitchen, H. n., Knight, S. n., Aldhouse, N. V., Macey, J. n., Nunes, F. P., Dutronc, Y. n., Mesinkovska, N. n., Ko, J. M., King, B. A. 2020


    Valid patient-reported outcome (PRO) measures are required to evaluate alopecia areata (AA) treatments.To develop a content-valid and clinically meaningful PRO measure to assess AA scalp hair loss with scores comparable with the five-response-level Alopecia Areata Investigator Global Assessment (AA-IGA™).A draft PRO measure was developed based on input from 10 clinical experts in AA. The PRO measure was cognitively debriefed, modified and finalized through two rounds of qualitative semistructured interviews with patients with AA who had experienced ≥ 50% scalp hair loss. Data were thematically analysed.Adults (round 1: n = 25; round 2: n = 15) and adolescents aged 15-17 years (round 1: n = 5) in North America participated. All patients named scalp hair loss as a key AA sign or symptom. Patients demonstrated the ability to self-report their current amount of scalp hair using percentages. In round 1 not all patients interpreted the measurement concept consistently; therefore, the PRO was modified to clarify the measurement concept to improve usability. Following modifications, patients in round 2 responded without difficulty to the PRO measure. Patients confirmed that they could use the five-level response scale to rate their scalp hair loss: no missing hair, 0%; limited, 1-20%; moderate, 21-49%; large, 50-94%; nearly all or all, 95-100%. Almost all patients deemed hair regrowth resulting in ≤ 20% scalp hair loss a treatment success.The Scalp Hair Assessment PRO™ is a content-valid, clinically meaningful assessment of distinct gradations of scalp hair loss for evaluating AA treatment for patients with ≥ 50% hair loss at baseline.

    View details for DOI 10.1111/bjd.19024

    View details for PubMedID 32163589

  • Development of Clinician-Reported Outcome (ClinRO) and Patient-Reported Outcome (PRO) Measures for Eyebrow, Eyelash and Nail Assessment in Alopecia Areata. American journal of clinical dermatology Wyrwich, K. W., Kitchen, H. n., Knight, S. n., Aldhouse, N. V., Macey, J. n., Nunes, F. P., Dutronc, Y. n., Mesinkovska, N. n., Ko, J. M., King, B. A. 2020


    Eyebrow and eyelash hair loss and nail damage-in addition to scalp hair loss-are important signs/symptoms of alopecia areata (AA) to patients and deserve assessment in AA clinical trials.Our objective was to develop clinician-reported outcome (ClinRO) and patient-reported outcome (PRO) measures and accompanying photoguides to aid in the assessment of AA-related eyebrow, eyelash and nail signs/symptoms.Iterative rounds of qualitative, semi-structured interviews were conducted with US expert dermatologists and North American patients with AA. Patients with eyebrow, eyelash and nail involvement were purposefully sampled. Interview transcripts were qualitatively analyzed.Dermatologists (n = 10) described eyebrow and eyelash loss as concerning for affected patients and, along with nail appearance, as deserving assessment. Dermatologist data informed the development of single item, 4-point Likert-type ClinRO and PRO measures of current eyebrow loss, eyelash loss and nail appearance and a PRO measure of eye irritation. Patients (n = 45, age 15-72 years) confirmed the importance and relevance of these signs/symptoms. Interim revision resulted in measures that were understood by and relevant to patients. Dermatologists (n = 5) and patients (n = 10, age 21-54 years) participated in the development of the eyebrow, eyelash and nail photoguides and confirmed that they included photos that appropriately represented different severity levels and were helpful to derive and standardize ratings across raters.The ClinRO and PRO measures for eyebrow, eyelash and nail appearance, with their accompanying photoguides and the PRO Measure for Eye Irritation provide clear and meaningful assessments of outcomes important to patients with AA.

    View details for DOI 10.1007/s40257-020-00545-9

    View details for PubMedID 32803546

  • "'You lose your hair, what's the big deal?' I was so embarrassed, I was so self-conscious, I was so depressed:" a qualitative interview study to understand the psychosocial burden of alopecia areata. Journal of patient-reported outcomes Aldhouse, N. V., Kitchen, H. n., Knight, S. n., Macey, J. n., Nunes, F. P., Dutronc, Y. n., Mesinkovska, N. n., Ko, J. M., King, B. A., Wyrwich, K. W. 2020; 4 (1): 76


    Alopecia areata (AA) is characterized by hair loss that can affect the scalp and body. This study describes the psychosocial burden of AA.Participants diagnosed with AA who had experienced ≥50% scalp hair loss according to the Severity of Alopecia Tool (SALT) were identified by clinicians. A semi-structured interview guide, developed with expert clinician input, included open-ended questions to explore patients' experiences of living with AA. Data were thematically analyzed to identify concepts and relationships.Participants (n = 45, 58% female, mean age 33.3 years [range 15-72], mean SALT 67.2 [range 0-100]) described the AA diagnosis as "devastating". Both males and females reported emotional and psychological impacts of AA including feeling sad/depressed (n = 21), embarrassed/ashamed (n = 10) and angry/frustrated (n = 3). Patients felt helpless (n = 5) due to the unpredictability of disease recurrence, and anxious (n = 19) about judgement from others. Many patients avoided social situations (n = 18), which impaired relationships and increased isolation. Coping strategies included concealment of hair loss through wigs or make-up, although fear of the displacement of these coverings also caused anxiety and the avoidance of activities that could result in scalp exposure (n = 22). Some patients became more accepting of AA over time, which lessened the emotional impact, though efficacious treatment was still desired. A conceptual framework was developed, and a conceptual model was created to depict the relationship between the physical signs/symptoms and the associated psychosocial effects of AA.AA impairs patients' emotional and psychological wellbeing, relationships and lifestyles. Greater disease awareness and effective treatments are needed.

    View details for DOI 10.1186/s41687-020-00240-7

    View details for PubMedID 32914253

  • Inflammatory alopecia in patients on dupilumab: a retrospective cohort study at an academic institution. Journal of the European Academy of Dermatology and Venereology : JEADV Zhu, G. A., Kang, K. J., Chen, J. K., Novoa, R. A., Brown, R. A., Chiou, A. S., Ko, J. M., Honari, G. 2019


    Dupilumab targets IL-4Ralpha and is used for moderate-to-severe atopic dermatitis (AD). Prior reports have described new alopecia areata (AA),1 flaring of prior AA,2 as well as improvement or resolution of AA3 in patients treated with dupilumab. We conducted a retrospective cohort study to describe the natural history of prior or new inflammatory alopecia in patients on dupilumab.

    View details for DOI 10.1111/jdv.16094

    View details for PubMedID 31737955

  • The Alopecia Areata Investigator's Global Assessment (AA-IGA) scale: A measure for evaluating clinically meaningful success in clinical trials Wyrwich, K. W., Kitchen, H., Knight, S., Aldhouse, N. J., Macey, J., Nunes, F. P., Dutronc, Y., Mesinkovska, N. A., Ko, J. M., King, B. A. MOSBY-ELSEVIER. 2019: AB283
  • Marking the Path Toward Artificial Intelligence-Based Image Classification in Dermatology. JAMA dermatology Novoa, R. A., Gevaert, O., Ko, J. M. 2019

    View details for DOI 10.1001/jamadermatol.2019.1633

    View details for PubMedID 31411643

  • Assessment of the Development of New Regional Dermatoses in Patients Treated for Atopic Dermatitis With Dupilumab JAMA DERMATOLOGY Zhu, G., Chen, J. K., Chiou, A., Ko, J., Honari, G. 2019; 155 (7): 850–52
  • New regional dermatoses during dupilumab therapy for atopic dermatitis Zhu, G. A., Chen, J. K., Chiou, A., Ko, J. M., Honari, G. ELSEVIER SCIENCE INC. 2019: S92
  • Repeat patch testing in a patient with allergic contact dermatitis improved on dupilumab. JAAD case reports Zhu, G. A., Chen, J. K., Chiou, A., Ko, J., Honari, G. 2019; 5 (4): 336–38

    View details for PubMedID 30989102

  • Artificial intelligence and dermatology: opportunities, challenges, and future directions. Seminars in cutaneous medicine and surgery Schlessinger, D. I., Chhor, G., Gevaert, O., Swetter, S. M., Ko, J., Novoa, R. A. 2019; 38 (1): E31–37


    The application of artificial intelligence (AI) to medicine has considerable potential within dermatology, where the majority of diagnoses are based on visual pattern recognition. Opportunities for AI in dermatology include the potential to automate repetitive tasks; optimize time-consuming tasks; extend limited medical resources; improve interobserver reliability issues; and expand the diagnostic toolbox of dermatologists. To achieve the full potential of AI, however, developers must aim to create algorithms representing diverse patient populations; ensure algorithm output is ultimately interpretable; validate algorithm performance prospectively; preserve human-patient interaction when necessary; and demonstrate validity in the eyes of regulatory bodies.

    View details for PubMedID 31051021

  • Artificial intelligence and dermatology: opportunities, challenges, and future directions SEMINARS IN CUTANEOUS MEDICINE AND SURGERY Schlessinger, D. I., Chhor, G., Gevaert, O., Swetter, S. M., Ko, J., Novoa, R. A. 2019; 38 (1): E31–E37
  • Commentary: Position Statement on Augmented Intelligence (AuI). Journal of the American Academy of Dermatology Kovarik, C. n., Lee, I. n., Ko, J. n. 2019

    View details for DOI 10.1016/j.jaad.2019.06.032

    View details for PubMedID 31247221

  • Assessment of the Development of New Regional Dermatoses in Patients Treated for Atopic Dermatitis With Dupilumab. JAMA dermatology Zhu, G. A., Chen, J. K., Chiou, A. n., Ko, J. n., Honari, G. n. 2019

    View details for PubMedID 31042259

  • Rebound effect associated with JAK inhibitor use in the treatment of alopecia areata. Journal of the European Academy of Dermatology and Venereology : JEADV Gordon, S. C., Abudu, M., Zancanaro, P., Ko, J. M., Rosmarin, D. 2018


    Alopecia areata (AA) is a common autoimmune disease driven by Th1 cytokines characterized by non-scarring hair loss.1,2 Mouse models have demonstrated that IFN-gamma-producing NKG2D+ CD8+ cytotoxic T lymphocytes (CTLs) are essential for disease pathogenesis, along with JAK-STAT dependent cytokines IFN-gamma and IL-15, which induce autoreactive T cell activation.1,3 This article is protected by copyright. All rights reserved.

    View details for DOI 10.1111/jdv.15383

    View details for PubMedID 30520145

  • Implementation and evaluation of Stanford Health Care store-and-forward teledermatology consultation workflow built within an existing electronic health record system. Journal of telemedicine and telecare Kim, G. E., Afanasiev, O. K., O'Dell, C., Sharp, C., Ko, J. M. 2018: 1357633X18799805


    Introduction Teledermatology services that function separately from patients' primary electronic health record (EHR) can lead to fragmented care, poor provider communication, privacy concerns and billing challenges. This study addresses these challenges by developing PhotoCareMD, a store-and-forward (SAF) teledermatology consultation workflow built entirely within an existing Epic-based EHR. Methods Thirty-six primary care physicians (PCPs) from eight outpatient clinics submitted 215 electronic consults (eConsults) for 211 patients to a Stanford Health Care dermatologist via PhotoCareMD. Comparisons were made with in-person referrals for this same dermatologist prior to initiation of PhotoCareMD. Results Compared to traditional in-person dermatology clinic visits, eConsults decreased the time to diagnosis and treatment from 23 days to 16 hours. The majority (73%) of eConsults were resolved electronically. In-person referrals from PhotoCareMD (27%) had a 50% lower cancellation rate compared with traditional referrals (11% versus 22%). The average in-person visit and documentation was 25 minutes compared with 8 minutes for an eConsult. PhotoCareMD saved 13 additional clinic hours to be made available to the dermatologist over the course of the pilot. At four patients per hour, this opens 52 dermatology clinic slots. Over 96% of patients had a favourable experience and 95% felt this service saved them time. Among PCPs, 100% would recommend PhotoCareMD to their colleagues and 95% said PhotoCareMD was a helpful educational tool. Discussion An internal SAF teledermatology workflow can be effectively implemented to increase access to and quality of dermatologic care. Our workflow can serve as a successful model for other hospitals and specialties.

    View details for DOI 10.1177/1357633X18799805

    View details for PubMedID 30301409

  • Automated Classification of Skin Lesions: From Pixels to Practice. The Journal of investigative dermatology Narla, A., Kuprel, B., Sarin, K., Novoa, R., Ko, J. 2018; 138 (10): 2108–10


    The letters "Interpretation of the Outputs of Deep Learning Model trained with Skin Cancer Dataset" and "Automated Dermatological Diagnosis: Hype or Reality?" highlight the opportunities, hurdles, and possible pitfalls with the development of tools that allow for automated skin lesion classification. The potential clinical impact of these advances relies on their scalability, accuracy, and generalizability across a range of diagnostic scenarios.

    View details for PubMedID 30244720

  • Automated Classification of Skin Lesions: From Pixels to Practice JOURNAL OF INVESTIGATIVE DERMATOLOGY Narla, A., Kuprel, B., Sarin, K., Novoa, R., Ko, J. 2018; 138 (10): 2108-2110
  • The importance of eyebrows in the treatment of alopecia areata: An online questionnaire Liu, L., King, B., Ko, J. MOSBY-ELSEVIER. 2018: AB288
  • Challenges and recommendations for epigenomics in precision health NATURE BIOTECHNOLOGY Carter, A. C., Chang, H. Y., Church, G., Dombkowski, A., Ecker, J. R., Gil, E., Giresi, P. G., Greely, H., Greenleaf, W. J., Hacohen, N., He, C., Hill, D., Ko, J., Kohane, I., Kundaje, A., Palmer, M., Snyder, M. P., Tung, J., Urban, A., Vidal, M., Wong, W. 2017; 35 (12): 1128–32

    View details for PubMedID 29220033

  • Dermatologist-level classification of skin cancer with deep neural networks. Nature Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., Thrun, S. 2017; 542 (7639): 115-118


    Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images-two orders of magnitude larger than previous datasets-consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.

    View details for DOI 10.1038/nature21056

    View details for PubMedID 28117445

  • Crowdsourcing dermatology: DataDerm, big data analytics, and machine learning technology. Journal of the American Academy of Dermatology Park, A. J., Ko, J. M., Swerlick, R. A. 2017

    View details for PubMedID 29042152

  • A case and review of congenital leukonychia. Dermatology online journal Pathipati, A. S., Ko, J. M., Yost, J. M. 2016; 22 (10)


    Leukonychia refers to a white discoloration of the nails. Although several conditions may cause white nails, a rare, isolated, congenital form of the disease is hypothesized to stem from disordered keratinization of the nail plate. Herein, we report a case of a 41-year-old woman with congenital leukonychia and review prior cases.

    View details for PubMedID 28329587

  • Safety and efficacy of the JAK inhibitor tofacitinib citrate in patients with alopecia areata. JCI insight Kennedy Crispin, M., Ko, J. M., Craiglow, B. G., Li, S., Shankar, G., Urban, J. R., Chen, J. C., Cerise, J. E., Jabbari, A., Winge, M. C., Marinkovich, M. P., Christiano, A. M., Oro, A. E., King, B. A. 2016; 1 (15)


    Alopecia areata (AA) is an autoimmune disease characterized by hair loss mediated by CD8(+) T cells. There are no reliably effective therapies for AA. Based on recent developments in the understanding of the pathomechanism of AA, JAK inhibitors appear to be a therapeutic option; however, their efficacy for the treatment of AA has not been systematically examined.This was a 2-center, open-label, single-arm trial using the pan-JAK inhibitor, tofacitinib citrate, for AA with >50% scalp hair loss, alopecia totalis (AT), and alopecia universalis (AU). Tofacitinib (5 mg) was given twice daily for 3 months. Endpoints included regrowth of scalp hair, as assessed by the severity of alopecia tool (SALT), duration of hair growth after completion of therapy, and disease transcriptome.Of 66 subjects treated, 32% experienced 50% or greater improvement in SALT score. AA and ophiasis subtypes were more responsive than AT and AU subtypes. Shorter duration of disease and histological peribulbar inflammation on pretreatment scalp biopsies were associated with improvement in SALT score. Drug cessation resulted in disease relapse in 8.5 weeks. Adverse events were limited to grade I and II infections. An AA responsiveness to JAK/STAT inhibitors score was developed to segregate responders and nonresponders, and the previously developed AA disease activity index score tracked response to treatment.At the dose and duration studied, tofacitinib is a safe and effective treatment for severe AA, though it does not result in a durable response. Transcriptome changes reveal unexpected molecular complexity within the NCT02197455 and NCT02312882.This work was supported by the US Department of Veterans Affairs Office of Research and Development, National Institute of Arthritis and Musculoskeletal and Skin Diseases National Institutes of Health grant R01 AR47223 and U01 AR67173, the National Psoriasis Foundation, the Swedish Society of Medicine, the Fernström Foundation, the Locks of Love Foundation, the National Alopecia Areata Foundation, and the Ranjini and Ajay Poddar Resource Fund for Dermatologic Diseases Research.

    View details for PubMedID 27699252

  • Safety and efficacy of the JAK inhibitor tofacitinib citrate in patients with alopecia areata JCI INSIGHT Crispin, M., Ko, J. M., Craiglow, B. G., Li, S., Shankar, G., Urban, J. R., Chen, J. C., Cerise, J. E., Jabbari, A., Winge, M. G., Marinkovich, M., Christiano, A. M., Oro, A. E., King, B. A. 2016; 1 (15)
  • Implementation of Stanford Health Care direct-care teledermatology program Pathipati, A., Ko, J. MOSBY-ELSEVIER. 2016: AB106
  • Implementation and evaluation of Stanford Health Care direct-care teledermatology program. SAGE open medicine Pathipati, A. S., Ko, J. M. 2016; 4: 2050312116659089-?


    Teledermatology has proven to be an effective means of providing dermatologic care. The existing research has primarily evaluated its usefulness in a consultative model. Few academic centers have evaluated a patient-initiated model, and direct-to-consumer services remain the subject of controversy. Stanford Health Care recently launched a direct-care, patient-initiated teledermatology pilot program. This article evaluates the viability and patient satisfaction with this service.During the pilot period, patients were able to seek remote dermatologic care using an eVisit tool in their MyHealth account. Patients initiated the consultation, answered questions regarding their complaint, and uploaded a picture if relevant. A Stanford dermatologist reviewed each eVisit and responded with an assessment and plan. The dermatologist noted whether they were able to make a diagnosis and their level of confidence in it. After the study, 10 patients participated in a focus group to provide feedback on the service.In all, 38 patients sought care during the pilot period. A dermatologist was able to make a diagnosis in 36 of 38 (95%) cases, with an average confidence level of 7.9 of 10. The average time to consultation was 0.8 days. Patients indicated high levels of satisfaction with the service although they had suggestions for improvement.Patients provided clinically useful images and information in a direct-care teledermatology model. Such services allow dermatology providers to increase access while maintaining high-quality care in an academic medical center. Further research is needed on standalone services that cannot integrate encounters with the patient's existing medical record.

    View details for DOI 10.1177/2050312116659089

    View details for PubMedID 27493756

    View details for PubMedCentralID PMC4959300

  • Validation of a Skin-Lesion Image-Matching Algorithm Based on Computer Vision Technology TELEMEDICINE AND E-HEALTH Chen, R. H., Snorrason, M., Enger, S. M., Mostafa, E., Ko, J. M., Aoki, V., Bowling, J. 2016; 22 (1): 45-50


    Melanoma incidence is increasing globally, but consistently accurate skin-lesion classification methods remain elusive. We developed a simple software system to classify potentially all types of skin lesions. In the current study, we evaluated the system's ability to identify melanomas with a diameter of 10 mm or larger.The skin-lesion classification system is composed of a proprietary database of nearly 12,000 diagnosed skin-lesion images and a computer algorithm based on the principles of content-based image retrieval. The algorithm compares characteristics of new skin-lesion images with images in the database to identify the nearest-match diagnosis.Nearly all classification accuracy measures for this new system exceeded 90%, with results for sensitivity of 90.4% (95% confidence interval, 85.6-93.7%), specificity of 91.5% (85.4-95.2%), positive predictive value of 94.5% (90.4-96.9%), negative predictive value of 85.5% (78.7-90.4%), and overall classification accuracy of 90.8% (87.2-93.4%).The image-matching algorithm performed with high accuracy for the classification of larger melanomas. Furthermore, the system does not require a dermoscope or any other specialized hardware; any close-focusing camera will do. This system has the potential to be an inexpensive and accurate tool for the evaluation of skin lesions in ethnically and geographically diverse populations.

    View details for DOI 10.1089/tmj.2014.0249

    View details for Web of Science ID 000368448900007

  • DermLens: Device for mobile teledermatology 73rd Annual Meeting of the American-Academy-of-Dermatology Lai, I., Ko, J., Pathipati, A. MOSBY-ELSEVIER. 2015: AB88–AB88
  • Randomized Controlled Trial of Cryotherapy With Liquid Nitrogen vs Topical Salicylic Acid vs Wait-and-See for Cutaneous Warts ARCHIVES OF DERMATOLOGY Ko, J., Bigby, M. 2012; 148 (7): 840-842

    View details for Web of Science ID 000306417100014

    View details for PubMedID 22801618

  • A Randomized, Prospective Trial Evaluating Surgeon Preference in Selection of Absorbable Suture Material JOURNAL OF DRUGS IN DERMATOLOGY Lu, L. K., Ko, J. M., Lee, J., Krum, D. M., Price, L. L., Finn, D., Lee, D., Rogers, G. S. 2012; 11 (2): 196-201


    This study is the first double-blinded, randomized comparison of two absorbable sutures. To better understand product characteristics and surgeon preference, we conducted a study of two similar-appearing FDA-approved sutures, glyconate and poliglecaprone 25. Four dermatologic surgeons were enlisted. A total of 48 patients with 53 surgical sites were examined. One half of each surgical wound was closed with one type of suture and the other half with the other type. Each half was evaluated for product characteristics. There was no statistically significant difference in surgeon preference for glyconate versus poliglecaprone 25 (P=0.64). Of the cohort preferring poliglecaprone 25, there was a correlation with speed of closure (P=0.06). Of the surgeons that preferred glyconate, we found significantly better visibility (P=0.03), reduced suture breakage during knot tying (P=0.05), and correlation with better handling properties (P=0.06) associated with that preference. The data from this study will enable products to be designed towards these needs and allow surgeons to select sutures that more precisely fit their particular requirements.

    View details for Web of Science ID 000299965700009

    View details for PubMedID 22270202

  • A new era: melanoma genetics and therapeutics JOURNAL OF PATHOLOGY Ko, J. M., Fisher, D. E. 2011; 223 (2): 241-250


    We have recently witnessed an explosion in our understanding of melanoma. Knowledge of the molecular basis of melanoma and the successes of targeted therapies have pushed melanoma care to the precipice of a new era. Identification of significant pathways and oncogenes has translated to the development of targeted therapies, some of which have produced major clinical responses. In this review, we provide an overview of selected key pathways and melanoma oncogenes as well as the targeted agents and therapeutic approaches whose successes suggest the promise of a new era in melanoma and cancer therapy. Despite these advances, the conversion of transient remissions to stable cures remains a vital challenge. Continued progress towards a better understanding about the complexity and redundancy responsible for melanoma progression may provide direction for anti-cancer drug development.

    View details for DOI 10.1002/path.2804

    View details for Web of Science ID 000285554600013

    View details for PubMedID 21125678

  • Pathways to Melanoma SEMINARS IN CUTANEOUS MEDICINE AND SURGERY Ko, J. M., Velez, N. F., Tsao, H. 2010; 29 (4): 210-217


    Melanoma is one of the most aggressive and yet poorly understood of human malignancies. Advances in genomics has allowed a more nuanced understanding of the disease, moving beyond the traditional dysplastic nevus-to-melanoma model and identifying multiple divergent oncogenic pathways leading to melanoma. An understanding of the molecular mechanisms driving melanoma has opened the doors for the development of targeted therapeutic approaches. As we enter the era of personalized medicine, it will be critical for clinicians to both appreciate and be able to determine the molecular profile of their patients' melanoma because this profile will guide risk stratification, genetic counseling, and treatment customization. A review of the divergent pathways of melanoma development is presented here, with a particular emphasis on recently identified mutations, and their implications for patient care.

    View details for DOI 10.1016/j.sder.2010.10.004

    View details for Web of Science ID 000287382200003

    View details for PubMedID 21277534

  • Skin and bone: the pathogenetic relationship between psoriasis and psoriatic arthritis GIORNALE ITALIANO DI DERMATOLOGIA E VENEREOLOGIA Ko, J. M., QURESHI, A. W. 2010; 145 (3): 393-406


    Our current understanding of the relationship between psoriasis and psoriatic arthritis remains incomplete, though the evidence from the clinical setting, response to therapeutics, epidemiology, genetics, imaging, and immunopathologic models suggest that they make likely share a common pathogenesis. Psoriatic disease can no longer be thought of as a condition limited to skin and joints. Rather, it must be considered a multi-faceted disorder in which systemic inflammation plays a central role. There is now convincing evidence that individuals with psoriasis have a higher prevalence of co-morbid disease, particularly cardiovascular risk factors, metabolic disorders, and other immune-mediated inflammatory diseases. The cutaneous manifestations of psoriasis place dermatologists in a crucial and privileged role--one that affords us the potential for early detection of associated co-morbid conditions through screening and perhaps impact disease course and clinical outcomes.

    View details for Web of Science ID 000279937700007

    View details for PubMedID 20461047

  • Paying for Enhanced Service Comparing Patients' Experiences in a Concierge and General Medicine Practice PATIENT-PATIENT CENTERED OUTCOMES RESEARCH Ko, J. M., Rodriguez, H. P., Fairchild, D. G., Rodday, A. M., Safran, D. G. 2009; 2 (2): 95-103


    Concierge medical practice is a relatively new and somewhat controversial development in primary-care practice. These practices promise patients more personalized care and dedicated service, in exchange for an annual membership fee paid by patients. The experiences of patients using these practices remain largely undocumented.To assess the experiences of patients in a concierge medicine practice compared with those in a general medicine practice.Stratified random samples of patients empanelled to each of the four doctors who practice at both a general medicine and a concierge medicine practice separately situated at an academic medical center were drawn. Patients were eligible for the study if they had a visit with the physician between January and May 2006. The study questionnaire (Consumer Assessment of Healthcare Providers and Systems Clinician and Group Survey, supplemented with items from the Ambulatory Care Experiences Survey) was administered by mail to 100 general medicine patients per physician (n = 400) and all eligible concierge medicine patients (n = 201). Patients who completed the survey and affirmed the study physician as their primary-care physician formed the analytic sample (n = 344) that was used to compare the experiences of concierge medicine and general medicine patients. Models controlled for respondent characteristics and accounted for patient clustering within physicians using physician fixed effects.Patients' experiences with organizational features of care, comprising care co-ordination (p < 0.01), access to care (p < 0.001) and interactions with office staff (p < 0.001), favored concierge medicine over general medicine practice. The quality of physician-patient interactions did not differ significantly between the two groups. However, the patients of the concierge medicine practice were more likely to report that their physician spends sufficient time in clinical encounters than patients of the general medicine practice (p < 0.003).The results suggest patients of the concierge medicine practice experienced and reported enhanced service, greater access to care, and better care co-ordination than those of the general medicine practice. This suggests that further study to understand the etiology of these differences may be beneficial in enhancing patients' experience in traditional primary-care practices.

    View details for DOI 10.2165/01312067-200902020-00005

    View details for Web of Science ID 000208001400005

    View details for PubMedID 22273085

  • Induction and exacerbation of psoriasis with TNF-blockade therapy: A review and analysis of 127 cases JOURNAL OF DERMATOLOGICAL TREATMENT Ko, J. M., Gottlieb, A. B., Kerbleski, J. F. 2009; 20 (2): 100-108


    There are reports of rare adverse effects of tumor necrosis factor (TNF) inhibitors, including infections, malignancies, and induction of autoimmune conditions. Intriguing, are cases of induction or exacerbation of psoriasis in conjunction with TNF inhibitor therapy, given that they are approved for treatment of the same condition. Objective: Published cases of psoriasis occurring during anti-TNF therapy were analyzed, including overviews of proposed etiologies and treatment recommendations.A literature search using Ovid MEDLINE and PubMed was performed for articles published between January 1990 and September 2007 to collect reported cases of psoriasis in patients receiving therapy with TNF blocking agents.A total of 127 cases were identified: 70 in patients on infliximab (55.1%), 35 with etanercept (27.6%), and 22 with adalimumab (17.3%). Females comprised 58% of cases; mean age of reported patients was 45.8 years, and the time from initiation of treatment to onset of lesions averaged 10.5 months. These patients suffered from a number of primary conditions, with rheumatoid arthritis, ankylosing spondylitis, and Crohn's disease accounting for the vast majority. Palmoplantar pustular psoriasis was observed in 40.5% of the cases, with plaque-type psoriasis in 33.1%, and other types comprising the remainder. Topical corticosteroids were the most commonly employed treatment modality but led to resolution in only 26.8% of cases in which they were employed solely. Switching to a different anti-TNF agent led to resolution in 15.4% of cases. Cessation of anti-TNF therapy with systemic therapy led to resolution in 64.3% of cases.More information and cases are needed. Biopsies of TNF-blockade-induced lesions may reveal what cytokines and cell types drive the development of these lesions. Additionally, there is a need to develop an algorithm to treat this paradoxical side effect of therapy with TNF-blockers.

    View details for DOI 10.1080/09546630802441234

    View details for Web of Science ID 000264554200008

    View details for PubMedID 18923992

  • Prospective assessment of computer-aided detection in interpretation of screening mammography AMERICAN JOURNAL OF ROENTGENOLOGY Ko, J. M., Nicholas, M. J., Mendel, J. B., Slanetz, P. J. 2006; 187 (6): 1483-1491


    The purpose of this study was to prospectively assess the usefulness of computer-aided detection (CAD) in the interpretation of screening mammography and to provide the true sensitivity and specificity of this technique in a clinical setting.Over a 26-month period, 5,016 screening mammograms were interpreted without, and subsequently with, the assistance of the iCAD MammoReader detection system. Data collected for actionable findings included dominant feature (calcification, mass, asymmetry, architectural distortion), detection method (radiologist only, CAD only, or both radiologist and CAD), BI-RADS assessment code, associated histopathology for those undergoing biopsy, and tumor stage for malignant lesions. The study population was cross-checked against an independent reference standard to identify false-negative cases.Of the 5,016 cases, the recall rate increased from 12% to 14% with the addition of CAD. Of the 107 (2%) patients who underwent biopsy, 101 (94%) were prompted by the radiologist and six (6%) were prompted by CAD. Of the 124 biopsies performed on actionable findings in the 107 patients, findings in 79 (64%) were benign and in 45 (36%) were in situ or invasive carcinoma. Three study participants who were not recalled by the radiologist with the assistance of CAD developed cancer within 1 year of the screening mammogram and were considered to be false-negative cases. The radiologist detected 43 (90%) of the 48 total malignancies and 45 (94%) of the 48 malignancies with the assistance of CAD. CAD missed eight cancers that were detected by the radiologist, which presented as architectural distortions (n = 3), irregular masses (n = 4), and a circumscribed mass (n = 1). CAD detected two in situ cancers as a faint cluster of calcifications that had not been perceived by the radiologist and one mass that was dismissed by the radiologist, accounting for at least a 4.7% increase in cancer detection rate. Sensitivity of screening mammography with the use of CAD (94%) represented an absolute and relative 4% increase over the sensitivity of the radiologist alone (90%). Specificity of screening mammography with and without the use of CAD was 99%.Routine use of CAD while interpreting screening mammograms significantly increases recall rates, has no significant effect on positive predictive value for biopsy, and can increase cancer detection rate by at least 4.7% and sensitivity by at least 4%. This study provides "true" values for sensitivity and specificity for use of CAD in interpretation of screening mammography as measured prospectively in the context of a working clinical setting.

    View details for DOI 10.2214/AJR.05.1582

    View details for Web of Science ID 000242289200017

    View details for PubMedID 17114541