All Publications

  • 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

  • The Promises and Perils of Foundation Models in Dermatology. The Journal of investigative dermatology Gui, H., Omiye, J. A., Chang, C. T., Daneshjou, R. 2024


    Foundation models (FM), which are large-scale artificial intelligence (AI) models that can complete a range of tasks, represent a paradigm shift in AI. These versatile models encompass large language models, vision-language models, and multimodal models. Although these models are often trained for broad tasks, they have been applied either out of the box or after additional fine tuning to tasks in medicine, including dermatology. From addressing administrative tasks to answering dermatology questions, these models are poised to have an impact on dermatology care delivery. As FMs become more ubiquitous in health care, it is important for clinicians and dermatologists to have a basic understanding of how these models are developed, what they are capable of, and what pitfalls exist. In this paper, we present a comprehensive yet accessible overview of the current state of FMs and summarize their current applications in dermatology, highlight their limitations, and discuss future developments in the field.

    View details for DOI 10.1016/j.jid.2023.12.019

    View details for PubMedID 38441507

  • Large Language Models in Medicine: The Potentials and Pitfalls : A Narrative Review. Annals of internal medicine Omiye, J. A., Gui, H., Rezaei, S. J., Zou, J., Daneshjou, R. 2024


    Large language models (LLMs) are artificial intelligence models trained on vast text data to generate humanlike outputs. They have been applied to various tasks in health care, ranging from answering medical examination questions to generating clinical reports. With increasing institutional partnerships between companies producing LLMs and health systems, the real-world clinical application of these models is nearing realization. As these models gain traction, health care practitioners must understand what LLMs are, their development, their current and potential applications, and the associated pitfalls in a medical setting. This review, coupled with a tutorial, provides a comprehensive yet accessible overview of these areas with the aim of familiarizing health care professionals with the rapidly changing landscape of LLMs in medicine. Furthermore, the authors highlight active research areas in the field that promise to improve LLMs' usability in health care contexts.

    View details for DOI 10.7326/M23-2772

    View details for PubMedID 38285984

  • Principles, applications, and future of artificial intelligence in dermatology. Frontiers in medicine Omiye, J. A., Gui, H., Daneshjou, R., Cai, Z. R., Muralidharan, V. 2023; 10: 1278232


    This paper provides an overview of artificial-intelligence (AI), as applied to dermatology. We focus our discussion on methodology, AI applications for various skin diseases, limitations, and future opportunities. We review how the current image-based models are being implemented in dermatology across disease subsets, and highlight the challenges facing widespread adoption. Additionally, we discuss how the future of AI in dermatology might evolve and the emerging paradigm of large language, and multi-modal models to emphasize the importance of developing responsible, fair, and equitable models in dermatology.

    View details for DOI 10.3389/fmed.2023.1278232

    View details for PubMedID 37901399

    View details for PubMedCentralID PMC10602645

  • Real-world agreement of same-visit Tono-Pen vs Goldmann applanation intraocular pressure measurements using electronic health records. Heliyon Gui, H., Zhang, Y., Chang, R. T., Wang, S. Y. 2023; 9 (8): e18703


    Purpose: To compare intraocular pressure (IOP) obtained with Tono-Pen (TP) and Goldmann applanation (GAT) using large-scale electronic health records (EHR).Design: Retrospective cohort study.Methods: A single pair of eligible TP/GAT IOP readings was randomly selected from the EHR for each ophthalmology patient at an academic ophthalmology center (2013-2022), yielding 4550 eligible measurements. We used Bland-Altman analysis to describe agreement between TP/GAT IOP differences and mean IOP measurements. We also used multivariable logistic regression to identify factors associated with different IOP readings in the same eye, including demographics, glaucoma diagnosis, and central corneal thickness (CCT). Primary outcome metrics were discrepant measurements between TP and GAT as defined by two methods: Outcome A (normal TP despite elevated GAT measurements), and Outcome B (TP and GAT IOP differences ≥6mmHg).Result: The mean TP/GAT IOP difference was 0.15mmHg (±5.49mmHg 95% CI). There was high correlation between the measurements (r=0.790, p<0.001). We found that TP overestimated pressures at IOP <16.5mmHg and underestimated at IOP >16.5mmHg (Fig. 4). Discrepant measurements accounted for 2.6% (N=116) and 5.2% (N=238) for outcomes A and B respectively. Patients with thinner CCT had higher odds of discrepant IOP (OR 0.88 per 25mum increase, CI [0.84-0.92], p<0.0001; OR 0.88 per 25mum increase, CI [0.84-0.92], p<0.0001 for outcomes A and B respectively).Conclusion: In a real-world academic practice setting, TP and GAT IOP measurements demonstrated close agreement, although 2.6% of measurements showed elevated GAT IOP despite normal TP measurements, and 5.2% of measurements were ≥6mmHg apart.

    View details for DOI 10.1016/j.heliyon.2023.e18703

    View details for PubMedID 37576221

  • Real-World Agreement of Same-Visit Tono-Pen Versus Goldmann Applanation Intraocular Pressure Measurements Using Electronic Health Records at an Academic Medical Center Zhang, Y., Gui, H., Chang, R., Wang, S. Y. ASSOC RESEARCH VISION OPHTHALMOLOGY INC. 2023
  • Prevalence of Contact Allergens in Natural Skin Care Products From US Commercial Retailers. JAMA dermatology Young, P. A., Gui, H., Bae, G. H. 2022

    View details for DOI 10.1001/jamadermatol.2022.3180

    View details for PubMedID 36103164

  • Fatty Acid Synthesis Knockdown Promotes Biofilm Wrinkling and Inhibits Sporulation in Bacillus subtilis. mBio Arjes, H. A., Gui, H., Porter, R., Atolia, E., Peters, J. M., Gross, C., Kearns, D. B., Huang, K. C. 2022: e0138822


    Many bacterial species typically live in complex three-dimensional biofilms, yet much remains unknown about differences in essential processes between nonbiofilm and biofilm lifestyles. Here, we created a CRISPR interference (CRISPRi) library of knockdown strains covering all known essential genes in the biofilm-forming Bacillus subtilis strain NCIB 3610 and investigated growth, biofilm colony wrinkling, and sporulation phenotypes of the knockdown library. First, we showed that gene essentiality is largely conserved between liquid and surface growth and between two media. Second, we quantified biofilm colony wrinkling using a custom image analysis algorithm and found that fatty acid synthesis and DNA gyrase knockdown strains exhibited increased wrinkling independent of biofilm matrix gene expression. Third, we designed a high-throughput screen to quantify sporulation efficiency after essential gene knockdown; we found that partial knockdowns of essential genes remained competent for sporulation in a sporulation-inducing medium, but knockdown of essential genes involved in fatty acid synthesis exhibited reduced sporulation efficiency in LB, a medium with generally lower levels of sporulation. We conclude that a subset of essential genes are particularly important for biofilm structure and sporulation/germination and suggest a previously unappreciated and multifaceted role for fatty acid synthesis in bacterial lifestyles and developmental processes. IMPORTANCE For many bacteria, life typically involves growth in dense, three-dimensional communities called biofilms that contain cells with differentiated roles held together by extracellular matrix. To examine how essential gene function varies between vegetative growth and the developmental states of biofilm formation and sporulation, we created and screened a comprehensive library of strains using CRISPRi to knockdown expression of each essential gene in the biofilm-capable Bacillus subtilis strain 3610. High-throughput assays and computational algorithms identified a subset of essential genes involved in biofilm wrinkling and sporulation and indicated that fatty acid synthesis plays important and multifaceted roles in bacterial development.

    View details for DOI 10.1128/mbio.01388-22

    View details for PubMedID 36069446

  • New-onset pemphigus vegetans and pemphigus foliaceus following SARS-CoV-2 vaccination: a case series. JAAD case reports Gui, H., Young, P. A., Sox, J., Pol-Rodriguez, M., Rieger, K. E., Lewis, M. A., Winge, M. C., Bae, G. H. 2022

    View details for DOI 10.1016/j.jdcr.2022.07.002

    View details for PubMedID 35845348

  • Explaining Deep Learning Models for Low Vision Prognosis Gui, H., Tseng, B., Hu, W., Wang, S. Y. ASSOC RESEARCH VISION OPHTHALMOLOGY INC. 2022
  • Looking for low vision: Predicting visual prognosis by fusing structured and free-text data from electronic health records. International journal of medical informatics Gui, H., Tseng, B., Hu, W., Wang, S. Y. 1800; 159: 104678


    INTRODUCTION: Low vision rehabilitation improves quality-of-life for visually impaired patients, but referral rates fall short of national guidelines. Automatically identifying, from electronic health records (EHR), patients with poor visual prognosis could allow targeted referrals to low vision services. The purpose of this study was to build and evaluate deep learning models that integrate EHR data that is both structured and free-text to predict visual prognosis.METHODS: We identified 5547 patients with low vision (defined as best documented visual acuity (VA)less than20/40) on≥1 encounter from EHR from 2009 to 2018, with≥1year of follow-up from the earliest date of low vision, who did not improve togreater than20/40 over 1year. Ophthalmology notes on or prior to the index date were extracted. Structured data available from the EHR included demographics, billing and procedure codes, medications, and exam findings including VA, intraocular pressure, corneal thickness, and refraction. To predict whether low vision patients would still have low vision a year later, we developed and compared deep learning models that used structured inputs and free-text progress notes. We compared three different representations of progress notes, including 1) using previously developed ophthalmology domain-specific word embeddings, and representing medical concepts from notes as 2) named entities represented by one-hot vectors and 3) named entities represented as embeddings. Standard performance metrics including area under the receiver operating curve (AUROC) and F1 score were evaluated on a held-out test set.RESULTS: Among the 5547 low vision patients in our cohort, 40.7% (N=2258) never improved to better than 20/40 over one year of follow-up. Our single-modality deep learning model based on structured inputs was able to predict low vision prognosis with AUROC of 80% and F1 score of 70%. Deep learning models utilizing named entity recognition achieved an AUROC of 79% and F1 score of 63%. Deep learning models further augmented with free-text inputs using domain-specific word embeddings, were able to achieve AUROC of 82% and F1 score of 69%, outperforming all single- and multiple-modality models representing text with biomedical concepts extracted through named entity recognition pipelines.DISCUSSION: Free text progress notes within the EHR provide valuable information relevant to predicting patients' visual prognosis. We observed that representing free-text using domain-specific word embeddings led to better performance than representing free-text using extracted named entities. The incorporation of domain-specific embeddings improved the performance over structured models, suggesting that domain-specific text representations may be especially important to the performance of predictive models in highly subspecialized fields such as ophthalmology.

    View details for DOI 10.1016/j.ijmedinf.2021.104678

    View details for PubMedID 34999410

  • Types of information that patients with lung cancer with targetable driver mutations and their caregivers learn from online forums: Results of a qualitative study Petrillo, L. A., Zhou, A., Gui, H., Sommer, R., Lin, J., Nipp, R., Traeger, L., Greer, J. A., Temel, J. S. LIPPINCOTT WILLIAMS & WILKINS. 2021
  • Three-dimensional biofilm colony growth supports a mutualism involving matrix and nutrient sharing. eLife Arjes, H. A., Willis, L., Gui, H., Xiao, Y., Peters, J., Gross, C., Huang, K. C. 2021; 10


    Life in a three-dimensional biofilm is typical for many bacteria, yet little is known about how strains interact in this context. Here, we created essential-gene CRISPRi knockdown libraries in biofilm-forming Bacillus subtilis and measured competitive fitness during colony co-culture with wild type. Partial knockdown of some translation-related genes reduced growth rates and led to out-competition. Media composition led some knockdowns to compete differentially as biofilm versus non-biofilm colonies. Cells depleted for the alanine racemase AlrA died in monoculture but survived in a biofilm-colony co-culture via nutrient sharing. Rescue was enhanced in biofilm-colony co-culture with a matrix-deficient parent, due to a mutualism involving nutrient and matrix sharing. We identified several examples of mutualism involving matrix sharing that occurred in three-dimensional biofilm colonies but not when cultured in two dimensions. Thus, growth in a three-dimensional colony can promote genetic diversity through sharing of secreted factors and may drive evolution of mutualistic behavior.

    View details for DOI 10.7554/eLife.64145

    View details for PubMedID 33594973