Roxana Daneshjou, MD, PhD
Assistant Professor of Biomedical Data Science and of Dermatology
Department of Biomedical Data Science
Bio
Dr. Daneshjou studied Bioengineering at Rice University before matriculating to Stanford School of Medicine where she completed her MD and a PhD in Genetics with Dr. Russ Altman as part of the medical scientist training program. She completed dermatology residency at Stanford as part of the research track and completed a postdoc in Biomedical Data Science with Dr. James Zou. She currently is the assistant director of the Center of Excellence for Precision Heath & Pharmacogenomics, director of informatics for the Stanford Skin Innovation and Interventional Research Group (SIIRG), a founding member of the Translational AI in Dermatology (TRAIND) group, and a faculty affiliate of Human-centered Artificial Intelligence (HAI) and the AI in Medicine and Imaging (AIMI) centers.
Clinical Focus
- Dermatology
Academic Appointments
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Assistant Professor, Department of Biomedical Data Science
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Assistant Professor, Dermatology
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Member, Bio-X
Honors & Awards
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Stanford Medicine TEDMED Student Ambassador, TEDMED (2015)
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Resident Research Symposium 2019 Everett C. Fox Memorial Award, American Academy of Dermatology (2019)
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Paul and Daisy Soros Fellowship for New Americans, Paul and Daisy Soros Fellowship for New Americans (2014-2016)
Boards, Advisory Committees, Professional Organizations
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Social Media Editor, Journal of Investigative Dermatology (2020 - Present)
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Editorial Trainee, British Journal of Dermatology (2020 - 2020)
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Board of Trustees Member, Paul and Daisy Soros Fellowship for New Americans (2019 - Present)
Professional Education
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Board Certification: American Board of Dermatology, Dermatology (2020)
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Medical Education: Stanford University School of Medicine (2016) CA
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Residency: Stanford University Dermatology Residency (2020) CA
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Internship: Kaiser Permanente Santa Clara Internal Medicine Residency (2017) CA
2024-25 Courses
- Biomedical Informatics Student Seminar
BIOMEDIN 201 (Spr) -
Independent Studies (3)
- Directed Reading and Research
BIODS 299 (Aut, Win, Spr, Sum) - Graduate Research on Biomedical Data Science
BIODS 399 (Aut, Win, Spr, Sum) - Out-of-Department Advanced Research Laboratory in Bioengineering
BIOE 191X (Aut, Win, Spr)
- Directed Reading and Research
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Prior Year Courses
2023-24 Courses
- Biomedical Informatics Student Seminar
BIODS 201, BIOMEDIN 201 (Spr)
- Biomedical Informatics Student Seminar
Stanford Advisees
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Postdoctoral Faculty Sponsor
Vasiliki Bikia, Aaron Fanous -
Doctoral Dissertation Advisor (AC)
Ank Agarwal -
Doctoral (Program)
Michael James, Carli Langevin
All Publications
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Engaging industry effectively and ethically in artificial intelligence from the Augmented Artificial Intelligence Committee Standards Workgroup.
Journal of the American Academy of Dermatology
2024
View details for DOI 10.1016/j.jaad.2024.03.036
View details for PubMedID 38691074
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Transparent medical image AI via an image-text foundation model grounded in medical literature.
Nature medicine
2024
Abstract
Building trustworthy and transparent image-based medical artificial intelligence (AI) systems requires the ability to interrogate data and models at all stages of the development pipeline, from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. In the present study, we present a foundation model approach, named MONET (medical concept retriever), which learns how to connect medical images with text and densely scores images on concept presence to enable important tasks in medical AI development and deployment such as data auditing, model auditing and model interpretation. Dermatology provides a demanding use case for the versatility of MONET, due to the heterogeneity in diseases, skin tones and imaging modalities. We trained MONET based on 105,550 dermatological images paired with natural language descriptions from a large collection of medical literature. MONET can accurately annotate concepts across dermatology images as verified by board-certified dermatologists, competitively with supervised models built on previously concept-annotated dermatology datasets of clinical images. We demonstrate how MONET enables AI transparency across the entire AI system development pipeline, from building inherently interpretable models to dataset and model auditing, including a case study dissecting the results of an AI clinical trial.
View details for DOI 10.1038/s41591-024-02887-x
View details for PubMedID 38627560
View details for PubMedCentralID 9374341
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Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis.
NPJ digital medicine
2024; 7 (1): 78
Abstract
The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistance on human diagnostic decisions. Therefore, the aim of this systematic review and meta-analysis was to study the effect of AI assistance on the accuracy of skin cancer diagnosis. We searched PubMed, Embase, IEE Xplore, Scopus and conference proceedings for articles from 1/1/2017 to 11/8/2022. We included studies comparing the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. Summary estimates of sensitivity and specificity of diagnostic accuracy with versus without AI assistance were computed using a bivariate random effects model. We identified 2983 studies, of which ten were eligible for meta-analysis. For clinicians without AI assistance, pooled sensitivity was 74.8% (95% CI 68.6-80.1) and specificity was 81.5% (95% CI 73.9-87.3). For AI-assisted clinicians, the overall sensitivity was 81.1% (95% CI 74.4-86.5) and specificity was 86.1% (95% CI 79.2-90.9). AI benefitted medical professionals of all experience levels in subgroup analyses, with the largest improvement among non-dermatologists. No publication bias was detected, and sensitivity analysis revealed that the findings were robust. AI in the hands of clinicians has the potential to improve diagnostic accuracy in skin cancer diagnosis. Given that most studies were conducted in experimental settings, we encourage future studies to further investigate these potential benefits in real-life settings.
View details for DOI 10.1038/s41746-024-01031-w
View details for PubMedID 38594408
View details for PubMedCentralID 8237239
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Dermatologists' perspectives and usage of large language models in practice- an exploratory survey.
The Journal of investigative dermatology
2024
View details for DOI 10.1016/j.jid.2024.03.028
View details for PubMedID 38582369
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Current State of Dermatology Mobile Applications With Artificial Intelligence Features: A Scoping Review.
JAMA dermatology
2024
Abstract
Importance: With advancements in mobile technology and artificial intelligence (AI) methods, there has been a substantial surge in the availability of direct-to-consumer mobile applications (apps) claiming to aid in the assessment and management of diverse skin conditions. Despite widespread patient downloads, these apps exhibit limited evidence supporting their efficacy.Objective: To identify and characterize current English-language AI dermatology mobile apps available for download, focusing on aspects such as purpose, supporting evidence, regulatory status, clinician input, data privacy measures, and use of image data.Evidence Review: In this scoping review, both Apple and Android mobile app stores were systematically searched for dermatology-related apps that use AI algorithms. Each app's purpose, target audience, evidence-based claims, algorithm details, data availability, clinician input during development, and data usage privacy policies were evaluated.Findings: A total of 909 apps were initially identified. Following the removal of 518 duplicates, 391 apps remained. Subsequent review excluded 350 apps due to nonmedical nature, non-English languages, absence of AI features, or unavailability, ultimately leaving 41 apps for detailed analysis. The findings revealed several concerning aspects of the current landscape of AI apps in dermatology. Notably, none of the apps were approved by the US Food and Drug Administration, and only 2 of the apps included disclaimers for the lack of regulatory approval. Overall, the study found that these apps lack supporting evidence, input from clinicians and/or dermatologists, and transparency in algorithm development, data usage, and user privacy.Conclusions and Relevance: This scoping review determined that although AI dermatology mobile apps hold promise for improving access to care and patient outcomes, in their current state, they may pose harm due to potential risks, lack of consistent validation, and misleading user communication. Addressing challenges in efficacy, safety, and transparency through effective regulation, validation, and standardized evaluation criteria is essential to harness the benefits of these apps while minimizing risks.
View details for DOI 10.1001/jamadermatol.2024.0468
View details for PubMedID 38452263
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The Promises and Perils of Foundation Models in Dermatology.
The Journal of investigative dermatology
2024
Abstract
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
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Diagnosis and management of hidradenitis suppurativa: Analysis of US insurance claims data.
JAAD international
2024; 14: 29-30
View details for DOI 10.1016/j.jdin.2023.10.002
View details for PubMedID 38058457
View details for PubMedCentralID PMC10696258
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CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods
GENOME BIOLOGY
2024; 25 (1): 53
Abstract
The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.
View details for DOI 10.1186/s13059-023-03113-6
View details for Web of Science ID 001184832400002
View details for PubMedID 38389099
View details for PubMedCentralID PMC10882881
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Deep learning-aided decision support for diagnosis of skin disease across skin tones.
Nature medicine
2024
Abstract
Although advances in deep learning systems for image-based medical diagnosis demonstrate their potential to augment clinical decision-making, the effectiveness of physician-machine partnerships remains an open question, in part because physicians and algorithms are both susceptible to systematic errors, especially for diagnosis of underrepresented populations. Here we present results from a large-scale digital experiment involving board-certified dermatologists (n=389) and primary-care physicians (n=459) from 39 countries to evaluate the accuracy of diagnoses submitted by physicians in a store-and-forward teledermatology simulation. In this experiment, physicians were presented with 364 images spanning 46 skin diseases and asked to submit up to four differential diagnoses. Specialists and generalists achieved diagnostic accuracies of 38% and 19%, respectively, but both specialists and generalists were four percentage points less accurate for the diagnosis of images of dark skin as compared to light skin. Fair deep learning system decision support improved the diagnostic accuracy of both specialists and generalists by more than 33%, but exacerbated the gap in the diagnostic accuracy of generalists across skin tones. These results demonstrate that well-designed physician-machine partnerships can enhance the diagnostic accuracy of physicians, illustrating that success in improving overall diagnostic accuracy does not necessarily address bias.
View details for DOI 10.1038/s41591-023-02728-3
View details for PubMedID 38317019
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The Arrival of Artificial Intelligence Large Language Models and Vision-Language Models: A Potential to Possible Change in the Paradigm of Healthcare Delivery inDermatology.
The Journal of investigative dermatology
2024
View details for DOI 10.1016/j.jid.2023.10.046
View details for PubMedID 38300200
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Large Language Models in Medicine: The Potentials and Pitfalls : A Narrative Review.
Annals of internal medicine
2024
Abstract
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
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Disentangling Hype from Reality for Artificial Intelligence-Based Skin Cancer Diagnosis: Comment on a Narrative Review.
The Journal of investigative dermatology
2024
View details for DOI 10.1016/j.jid.2023.11.020
View details for PubMedID 38244023
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Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians.
Nature biomedical engineering
2023
Abstract
The inferences of most machine-learning models powering medical artificial intelligence are difficult to interpret. Here we report a general framework for model auditing that combines insights from medical experts with a highly expressive form of explainable artificial intelligence. Specifically, we leveraged the expertise of dermatologists for the clinical task of differentiating melanomas from melanoma 'lookalikes' on the basis of dermoscopic and clinical images of the skin, and the power of generative models to render 'counterfactual' images to understand the 'reasoning' processes of five medical-image classifiers. By altering image attributes to produce analogous images that elicit a different prediction by the classifiers, and by asking physicians to identify medically meaningful features in the images, the counterfactual images revealed that the classifiers rely both on features used by human dermatologists, such as lesional pigmentation patterns, and on undesirable features, such as background skin texture and colour balance. The framework can be applied to any specialized medical domain to make the powerful inference processes of machine-learning models medically understandable.
View details for DOI 10.1038/s41551-023-01160-9
View details for PubMedID 38155295
View details for PubMedCentralID 7820258
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Empowering the next generation of artificial intelligence in dermatology: The datasets and benchmarks track of the Journal of Investigative Dermatology.
The Journal of investigative dermatology
2023
View details for DOI 10.1016/j.jid.2023.11.011
View details for PubMedID 38103828
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Black Box Warning: Large Language Models and the Future of Infectious Diseases Consultation.
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
2023
Abstract
Large language models (LLMs) are artificial intelligence systems trained by deep learning algorithms to process natural language and generate text responses to user prompts. Some approach physician performance on a range of medical challenges, leading some proponents to advocate for their potential use in clinical consultation and prompting some consternation about the future of cognitive specialties. However, LLMs currently have limitations that preclude safe clinical deployment in performing specialist consultations, including frequent confabulations, lack of contextual awareness crucial for nuanced diagnostic and treatment plans, inscrutable and unexplainable training data and methods, and propensity to recapitulate biases. Nonetheless, considering the rapid improvement in this technology, growing calls for clinical integration, and healthcare systems that chronically undervalue cognitive specialties, it is critical that infectious diseases clinicians engage with LLMs to enable informed advocacy for how they should-and shouldn't-be used to augment specialist care.
View details for DOI 10.1093/cid/ciad633
View details for PubMedID 37971399
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Large language models propagate race-based medicine.
NPJ digital medicine
2023; 6 (1): 195
Abstract
Large language models (LLMs) are being integrated into healthcare systems; but these models may recapitulate harmful, race-based medicine. The objective of this study is to assess whether four commercially available large language models (LLMs) propagate harmful, inaccurate, race-based content when responding to eight different scenarios that check for race-based medicine or widespread misconceptions around race. Questions were derived from discussions among four physician experts and prior work on race-based medical misconceptions believed by medical trainees. We assessed four large language models with nine different questions that were interrogated five times each with a total of 45 responses per model. All models had examples of perpetuating race-based medicine in their responses. Models were not always consistent in their responses when asked the same question repeatedly. LLMs are being proposed for use in the healthcare setting, with some models already connecting to electronic health record systems. However, this study shows that based on our findings, these LLMs could potentially cause harm by perpetuating debunked, racist ideas.
View details for DOI 10.1038/s41746-023-00939-z
View details for PubMedID 37864012
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Principles, applications, and future of artificial intelligence in dermatology.
Frontiers in medicine
2023; 10: 1278232
Abstract
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
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Generation of a Melanoma and Nevus Data Set From Unstandardized Clinical Photographs on the Internet.
JAMA dermatology
2023
Abstract
Artificial intelligence (AI) training for diagnosing dermatologic images requires large amounts of clean data. Dermatologic images have different compositions, and many are inaccessible due to privacy concerns, which hinder the development of AI.To build a training data set for discriminative and generative AI from unstandardized internet images of melanoma and nevus.In this diagnostic study, a total of 5619 (CAN5600 data set) and 2006 (CAN2000 data set; a manually revised subset of CAN5600) cropped lesion images of either melanoma or nevus were semiautomatically annotated from approximately 500 000 photographs on the internet using convolutional neural networks (CNNs), region-based CNNs, and large mask inpainting. For unsupervised pretraining, 132 673 possible lesions (LESION130k data set) were also created with diversity by collecting images from 18 482 websites in approximately 80 countries. A total of 5000 synthetic images (GAN5000 data set) were generated using the generative adversarial network (StyleGAN2-ADA; training, CAN2000 data set; pretraining, LESION130k data set).The area under the receiver operating characteristic curve (AUROC) for determining malignant neoplasms was analyzed. In each test, 1 of the 7 preexisting public data sets (total of 2312 images; including Edinburgh, an SNU subset, Asan test, Waterloo, 7-point criteria evaluation, PAD-UFES-20, and MED-NODE) was used as the test data set. Subsequently, a comparative study was conducted between the performance of the EfficientNet Lite0 CNN on the proposed data set and that trained on the remaining 6 preexisting data sets.The EfficientNet Lite0 CNN trained on the annotated or synthetic images achieved higher or equivalent mean (SD) AUROCs to the EfficientNet Lite0 trained using the pathologically confirmed public data sets, including CAN5600 (0.874 [0.042]; P = .02), CAN2000 (0.848 [0.027]; P = .08), and GAN5000 (0.838 [0.040]; P = .31 [Wilcoxon signed rank test]) and the preexisting data sets combined (0.809 [0.063]) by the benefits of increased size of the training data set.The synthetic data set in this diagnostic study was created using various AI technologies from internet images. A neural network trained on the created data set (CAN5600) performed better than the same network trained on preexisting data sets combined. Both the annotated (CAN5600 and LESION130k) and synthetic (GAN5000) data sets could be shared for AI training and consensus between physicians.
View details for DOI 10.1001/jamadermatol.2023.3521
View details for PubMedID 37792351
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Skin Tone Analysis for Representation in Educational Materials (STAR-ED) using machine learning.
NPJ digital medicine
2023; 6 (1): 151
Abstract
Images depicting dark skin tones are significantly underrepresented in the educational materials used to teach primary care physicians and dermatologists to recognize skin diseases. This could contribute to disparities in skin disease diagnosis across different racial groups. Previously, domain experts have manually assessed textbooks to estimate the diversity in skin images. Manual assessment does not scale to many educational materials and introduces human errors. To automate this process, we present the Skin Tone Analysis for Representation in EDucational materials (STAR-ED) framework, which assesses skin tone representation in medical education materials using machine learning. Given a document (e.g., a textbook in .pdf), STAR-ED applies content parsing to extract text, images, and table entities in a structured format. Next, it identifies images containing skin, segments the skin-containing portions of those images, and estimates the skin tone using machine learning. STAR-ED was developed using the Fitzpatrick17k dataset. We then externally tested STAR-ED on four commonly used medical textbooks. Results show strong performance in detecting skin images (0.96±0.02 AUROC and 0.90±0.06 F1 score) and classifying skin tones (0.87±0.01 AUROC and 0.91±0.00 F1 score). STAR-ED quantifies the imbalanced representation of skin tones in four medical textbooks: brown and black skin tones (Fitzpatrick V-VI) images constitute only 10.5% of all skin images. We envision this technology as a tool for medical educators, publishers, and practitioners to assess skin tone diversity in their educational materials.
View details for DOI 10.1038/s41746-023-00881-0
View details for PubMedID 37596324
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Best Practices for Clinical Skin Image Acquisition in Translational Artificial Intelligence Research.
The Journal of investigative dermatology
2023; 143 (7): 1127-1132
Abstract
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
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Dissection of medical AI reasoning processes via physician and generative-AI collaboration.
medRxiv : the preprint server for health sciences
2023
Abstract
Despite the proliferation and clinical deployment of artificial intelligence (AI)-based medical software devices, most remain black boxes that are uninterpretable to key stakeholders including patients, physicians, and even the developers of the devices. Here, we present a general model auditing framework that combines insights from medical experts with a highly expressive form of explainable AI that leverages generative models, to understand the reasoning processes of AI devices. We then apply this framework to generate the first thorough, medically interpretable picture of the reasoning processes of machine-learning-based medical image AI. In our synergistic framework, a generative model first renders "counterfactual" medical images, which in essence visually represent the reasoning process of a medical AI device, and then physicians translate these counterfactual images to medically meaningful features. As our use case, we audit five high-profile AI devices in dermatology, an area of particular interest since dermatology AI devices are beginning to achieve deployment globally. We reveal how dermatology AI devices rely both on features used by human dermatologists, such as lesional pigmentation patterns, as well as multiple, previously unreported, potentially undesirable features, such as background skin texture and image color balance. Our study also sets a precedent for the rigorous application of explainable AI to understand AI in any specialized domain and provides a means for practitioners, clinicians, and regulators to uncloak AI's powerful but previously enigmatic reasoning processes in a medically understandable way.
View details for DOI 10.1101/2023.05.12.23289878
View details for PubMedID 37292705
View details for PubMedCentralID PMC10246034
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Development and Clinical Evaluation of an Artificial Intelligence Support Tool for Improving Telemedicine Photo Quality.
JAMA dermatology
2023
Abstract
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
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Evaluation of diagnosis diversity in artificial intelligence datasets: a scoping review.
The British journal of dermatology
2023; 188 (2): 292-294
View details for DOI 10.1093/bjd/ljac047
View details for PubMedID 36763858
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Session Introduction: Precision Medicine: Using Artificial Intelligence to Improve Diagnostics and Healthcare.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
2023; 28: 257-262
Abstract
Precision medicine requires a deep understanding of complex biomedical and healthcare data, which is being generated at exponential rates and increasingly made available through public biobanks, electronic medical record systems and biomedical databases and knowledgebases. The complexity and sheer amount of data prohibit manual manipulation. Instead, the field depends on artificial intelligence approaches to parse, annotate, evaluate and interpret the data to enable applications to patient healthcare At the 2023 Pacific Symposium on Biocomputing (PSB) session entitled "Precision Medicine: Using Artificial Intelligence (AI) to improve diagnostics and healthcare", we spotlight research that develops and applies computational methodologies to solve biomedical problems.
View details for PubMedID 36540982
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Global dermatology talks is a virtual lecture series for equitable dissemination of dermatologic information.
JAAD international
2022; 9: 116-118
View details for DOI 10.1016/j.jdin.2022.08.020
View details for PubMedID 36248200
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Disparities in dermatology AI performance on a diverse, curated clinical image set.
Science advances
2022; 8 (32): eabq6147
Abstract
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
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Reducing Language Barriers in Dermatology: A Step Towards Equitable Care.
Journal of the American Academy of Dermatology
2022
View details for DOI 10.1016/j.jaad.2022.07.049
View details for PubMedID 35944811
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Toward Augmented Intelligence: The First Prospective, Randomized Clinical Trial Assessing Clinician and Artificial Intelligence Collaboration in Dermatology.
The Journal of investigative dermatology
2022
Abstract
Han etal. (2022) report the first randomized, prospective clinical trial in dermatology evaluating the performance of clinicians working in collaboration with artificial intelligence (AI). This foundational work shows the limitations of AI in a real-world clinical setting and shows its potential for improving the performance of nonspecialists diagnosing skin diseases.
View details for DOI 10.1016/j.jid.2022.03.019
View details for PubMedID 35659393
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Image Consent and the Development of Image-Based Artificial Intelligence-Reply.
JAMA dermatology
2022
View details for DOI 10.1001/jamadermatol.2022.0108
View details for PubMedID 35416913
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Precision Medicine: Using Artificial Intelligence to Improve Diagnostics and Healthcare.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
2022; 27: 223-230
Abstract
The continued generation of large amounts of data within healthcare-from imaging to electronic medical health records to genomics and multi-omics -necessitates tools and methods to parse and interpret these data to improve healthcare outcomes. Artificial intelligence, and in particular deep learning, has enabled researchers to gain new insights from large scale and multimodal data. At the 2022 Pacific Symposium on Biocomputing (PSB) session entitled "Precision Medicine: Using Artificial Intelligence to Improve Diagnostics and Healthcare", we showcase the latest research, influenced and inspired by the idea of using technology to build a more fair, tailored, and cost-effective healthcare system after the COVID-19 pandemic.
View details for PubMedID 34890151
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Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions
ASSOC COMPUTING MACHINERY. 2022: 763-777
View details for DOI 10.1145/3514094.3534150
View details for Web of Science ID 001118017500074
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Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology: CLEAR Derm Consensus Guidelines From the International Skin Imaging Collaboration Artificial Intelligence Working Group.
JAMA dermatology
2021
Abstract
Importance: The use of artificial intelligence (AI) is accelerating in all aspects of medicine and has the potential to transform clinical care and dermatology workflows. However, to develop image-based algorithms for dermatology applications, comprehensive criteria establishing development and performance evaluation standards are required to ensure product fairness, reliability, and safety.Objective: To consolidate limited existing literature with expert opinion to guide developers and reviewers of dermatology AI.Evidence Review: In this consensus statement, the 19 members of the International Skin Imaging Collaboration AI working group volunteered to provide a consensus statement. A systematic PubMed search was performed of English-language articles published between December 1, 2008, and August 24, 2021, for "artificial intelligence" and "reporting guidelines," as well as other pertinent studies identified by the expert panel. Factors that were viewed as critical to AI development and performance evaluation were included and underwent 2 rounds of electronic discussion to achieve consensus.Findings: A checklist of items was developed that outlines best practices of image-based AI development and assessment in dermatology.Conclusions and Relevance: Clinically effective AI needs to be fair, reliable, and safe; this checklist of best practices will help both developers and reviewers achieve this goal.
View details for DOI 10.1001/jamadermatol.2021.4915
View details for PubMedID 34851366
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Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms: A Scoping Review.
JAMA dermatology
2021
Abstract
Importance: Clinical artificial intelligence (AI) algorithms have the potential to improve clinical care, but fair, generalizable algorithms depend on the clinical data on which they are trained and tested.Objective: To assess whether data sets used for training diagnostic AI algorithms addressing skin disease are adequately described and to identify potential sources of bias in these data sets.Data Sources: In this scoping review, PubMed was used to search for peer-reviewed research articles published between January 1, 2015, and November 1, 2020, with the following paired search terms: deep learning and dermatology, artificial intelligence and dermatology, deep learning and dermatologist, and artificial intelligence and dermatologist.Study Selection: Studies that developed or tested an existing deep learning algorithm for triage, diagnosis, or monitoring using clinical or dermoscopic images of skin disease were selected, and the articles were independently reviewed by 2 investigators to verify that they met selection criteria.Consensus Process: Data set audit criteria were determined by consensus of all authors after reviewing existing literature to highlight data set transparency and sources of bias.Results: A total of 70 unique studies were included. Among these studies, 1 065 291 images were used to develop or test AI algorithms, of which only 257 372 (24.2%) were publicly available. Only 14 studies (20.0%) included descriptions of patient ethnicity or race in at least 1 data set used. Only 7 studies (10.0%) included any information about skin tone in at least 1 data set used. Thirty-six of the 56 studies developing new AI algorithms for cutaneous malignant neoplasms (64.3%) met the gold standard criteria for disease labeling. Public data sets were cited more often than private data sets, suggesting that public data sets contribute more to new development and benchmarks.Conclusions and Relevance: This scoping review identified 3 issues in data sets that are used to develop and test clinical AI algorithms for skin disease that should be addressed before clinical translation: (1) sparsity of data set characterization and lack of transparency, (2) nonstandard and unverified disease labels, and (3) inability to fully assess patient diversity used for algorithm development and testing.
View details for DOI 10.1001/jamadermatol.2021.3129
View details for PubMedID 34550305
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Research Techniques Made Simple: Scientific Communication using Twitter.
The Journal of investigative dermatology
2021; 141 (7): 1615
Abstract
The scientific process depends on social interactions: communication and dissemination of research findings, evaluation and discussion of scientific work, and collaboration with other scientists. Social media, and specifically, Twitter has accelerated the ability to accomplish these goals. We discuss the ways that Twitter is used by scientists and provide guidance on navigating the academic Twitter community.
View details for DOI 10.1016/j.jid.2021.03.026
View details for PubMedID 34167718
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How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals.
Nature medicine
2021
View details for DOI 10.1038/s41591-021-01312-x
View details for PubMedID 33820998
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Raising the bar for Randomized Trials involving Artificial Intelligence: The SPIRIT-AI and CONSORT-AI Guidelines.
The Journal of investigative dermatology
2021
Abstract
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
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How to evaluate deep learning for cancer diagnostics - factors and recommendations.
Biochimica et biophysica acta. Reviews on cancer
2021: 188515
Abstract
The large volume of data used in cancer diagnosis presents a unique opportunity for deep learning algorithms, which improve in predictive performance with increasing data. When applying deep learning to cancer diagnosis, the goal is often to learn how to classify an input sample (such as images or biomarkers) into predefined categories (such as benign or cancerous). In this article, we examine examples of how deep learning algorithms have been implemented to make predictions related to cancer diagnosis using clinical, radiological, and pathological image data. We present a systematic approach for evaluating the development and application of clinical deep learning algorithms. Based on these examples and the current state of deep learning in medicine, we discuss the future possibilities in this space and outline a roadmap for implementations of deep learning in cancer diagnosis.
View details for DOI 10.1016/j.bbcan.2021.188515
View details for PubMedID 33513392
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Diversity, Race, and Health
MED
2021; 2 (1): 6-10
View details for Web of Science ID 000658569000004
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TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
2021; 26: 220–31
Abstract
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
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Computational Challenges and Artificial Intelligence in Precision Medicine
WORLD SCIENTIFIC PUBL CO PTE LTD. 2021: 166-171
View details for Web of Science ID 000759784400016
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TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos
WORLD SCIENTIFIC PUBL CO PTE LTD. 2021: 220-231
View details for Web of Science ID 000759784400021
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Pernio-like eruption associated with COVID-19 in skin of color.
JAAD case reports
2020; 6 (9): 892–97
View details for DOI 10.1016/j.jdcr.2020.07.009
View details for PubMedID 32835046
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Twitter Journal Clubs: Medical Education in the Era of Social Media.
JAMA dermatology
2020
View details for DOI 10.1001/jamadermatol.2020.0315
View details for PubMedID 32186655
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Session Intro: ARTIFICIAL INTELLIGENCE FOR ENHANCING CLINICAL MEDICINE.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
2020; 25: 1–6
Abstract
Machine learning and deep learning have revolutionized our ability to analyze and find patterns in multi-dimensional and intricate datasets. As such, these methods have the ability to help us decipher the large volume of data generated within healthcare. These tools hold the promise of enhancing patient care through several modalities, including clinical decision support, monitoring tools, image interpretation, and triaging capabilities. For the 2020 Pacific Symposium on Biocomputing's session on Artificial Intelligence for Enhancing Clinical Medicine, we highlight novel research on the application of artificial intelligence to solve problems within the field of medicine.
View details for PubMedID 33381619
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ARTIFICIAL INTELLIGENCE FOR ENHANCING CLINICAL MEDICINE
WORLD SCIENTIFIC PUBL CO PTE LTD. 2020: 1-6
View details for Web of Science ID 000702064500001
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Increasing the visibility of dermatologic research contributions by women and underrepresented minorities.
Journal of the American Academy of Dermatology
2020
View details for DOI 10.1016/j.jaad.2020.07.038
View details for PubMedID 32682885
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Social Media: A New Tool for Scientific Engagement.
The Journal of investigative dermatology
2020; 140 (10): 1884–85
View details for DOI 10.1016/j.jid.2020.08.005
View details for PubMedID 32972520
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Genome-wide meta-analysis identifies eight new susceptibility loci for cutaneous squamous cell carcinoma.
Nature communications
2020; 11 (1): 820
Abstract
Cutaneous squamous cell carcinoma (SCC) is one of the most common cancers in the United States. Previous genome-wide association studies (GWAS) have identified 14 single nucleotide polymorphisms (SNPs) associated with cutaneous SCC. Here, we report the largest cutaneous SCC meta-analysis to date, representing six international cohorts and totaling 19,149 SCC cases and 680,049 controls. We discover eight novel loci associated with SCC, confirm all previously associated loci, and perform fine mapping of causal variants. The novel SNPs occur within skin-specific regulatory elements and implicate loci involved in cancer development, immune regulation, and keratinocyte differentiation in SCC susceptibility.
View details for DOI 10.1038/s41467-020-14594-5
View details for PubMedID 32041948
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Predicting venous thromboembolism risk from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges.
Human mutation
2019
Abstract
Genetics play a key role in venous thromboembolism (VTE) risk, however established risk factors in European populations do not translate to individuals of African descent due to differences in allele frequencies between populations. As part of the fifth iteration of the Critical Assessment of Genome Interpretation, participants were asked to predict VTE status in exome data from African American subjects. Participants were provided with 103 unlabeled exomes from patients treated with warfarin for non-VTE causes or VTE and asked to predict which disease each subject had been treated for. Given the lack of training data, many participants opted to use unsupervised machine learning methods, clustering the exomes by variation in genes known to be associated with VTE. The best performing method using only VTE related genes achieved an AUC of 0.65. Here we discuss the range of methods used in the prediction of VTE from sequence data and explore some of the difficulties of conducting a challenge with known confounders. Additionally, we show that an existing genetic risk score for VTE that was developed in European subjects works well in African Americans. This article is protected by copyright. All rights reserved.
View details for DOI 10.1002/humu.23825
View details for PubMedID 31140652
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Pharmacogenomics in dermatology: tools for understanding gene-drug associations.
Seminars in cutaneous medicine and surgery
2019; 38 (1): E19–E24
Abstract
Pharmacogenomics aims to associate human genetic variability with differences in drug phenotypes in order to tailor drug treatment to individual patients. The massive amount of genetic data generated from large cohorts of patients with variable drug phenotypes have led to advances in this field. Understanding the application of pharmacogenomics in dermatology could inform clinical practice and provide insight for future research. The Pharmacogenomics Knowledge Base and the Clinical Pharmacogenetics Implementation Consortium are among the resources to help clinicians and researchers navigate the many gene-drug associations that have already been discovered. The implementation of clinical pharmacogenomics within health care systems remains an area of ongoing development. This review provides an introduction to the field of pharmacogenomics and to current pharmacogenomics resources using examples of gene-drug associations relevant to the field of dermatology.
View details for DOI 10.12788/j.sder.2019.009
View details for PubMedID 31051019
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Pharmacogenomics and big genomic data: from lab to clinic and back again.
Human molecular genetics
2018; 27 (R1): R72–R78
Abstract
The field of pharmacogenomics is an area of great potential for near-term human health impacts from the big genomic data revolution. Pharmacogenomics research momentum is building with numerous hypotheses currently being investigated through the integration of molecular profiles of different cell lines and large genomic data sets containing information on cellular and human responses to therapies. Additionally, the results of previous pharmacogenetic research efforts have been formulated into clinical guidelines that are beginning to impact how healthcare is conducted on the level of the individual patient. This trend will only continue with the recent release of new datasets containing linked genotype and electronic medical record data. This review discusses key resources available for pharmacogenomics and pharmacogenetics research and highlights recent work within the field.
View details for PubMedID 29635477
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Pharmacogenomics and big genomic data: from lab to clinic and back again
HUMAN MOLECULAR GENETICS
2018; 27 (R1): R72–R78
View details for DOI 10.1093/hmg/ddy116
View details for Web of Science ID 000431884200012
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Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges
HUMAN MUTATION
2017; 38 (9): 1182–92
Abstract
Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.
View details for DOI 10.1002/humu.23280
View details for Web of Science ID 000407861100014
View details for PubMedID 28634997
View details for PubMedCentralID PMC5600620
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Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.
Genome medicine
2017; 9 (1): 98
Abstract
Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects.Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals.We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations.Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.
View details for PubMedID 29178968
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Population-specific single-nucleotide polymorphism confers increased risk of venous thromboembolism in African Americans.
Molecular genetics & genomic medicine
2016; 4 (5): 513-520
Abstract
African Americans have a higher incidence of venous thromboembolism (VTE) than European descent individuals. However, the typical genetic risk factors in populations of European descent are nearly absent in African Americans, and population-specific genetic factors influencing the higher VTE rate are not well characterized.We performed a candidate gene analysis on an exome-sequenced African American family with recurrent VTE and identified a variant in Protein S (PROS1) V510M (rs138925964). We assessed the population impact of PROS1 V510M using a multicenter African American cohort of 306 cases with VTE compared to 370 controls. Additionally, we compared our case cohort to a background population cohort of 2203 African Americans in the NHLBI GO Exome Sequencing Project (ESP).In the African American family with recurrent VTE, we found prior laboratories for our cases indicating low free Protein S levels, providing functional support for PROS1 V510M as the causative mutation. Additionally, this variant was significantly enriched in the VTE cases of our multicenter case-control study (Fisher's Exact Test, P = 0.0041, OR = 4.62, 95% CI: 1.51-15.20; allele frequencies - cases: 2.45%, controls: 0.54%). Similarly, PROS1 V510M was also enriched in our VTE case cohort compared to African Americans in the ESP cohort (Fisher's Exact Test, P = 0.010, OR = 2.28, 95% CI: 1.26-4.10).We found a variant, PROS1 V510M, in an African American family with VTE and clinical laboratory abnormalities in Protein S. Additionally, we found that this variant conferred increased risk of VTE in a case-control study of African Americans. In the ESP cohort, the variant is nearly absent in ESP European descent subjects (n = 3, allele frequency: 0.03%). Additionally, in 1000 Genomes Phase 3 data, the variant only appears in African descent populations. Thus, PROS1 V510M is a population-specific genetic risk factor for VTE in African Americans.
View details for DOI 10.1002/mgg3.226
View details for PubMedID 27652279
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ClinGen - The Clinical Genome Resource
NEW ENGLAND JOURNAL OF MEDICINE
2015; 372 (23): 2235-2242
View details for DOI 10.1056/NEJMsr1406261
View details for PubMedID 26014595
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PharmGKB summary: very important pharmacogene information for CYP4F2
PHARMACOGENETICS AND GENOMICS
2015; 25 (1): 41-47
View details for DOI 10.1097/FPC.0000000000000100
View details for Web of Science ID 000346632900006
View details for PubMedID 25370453
View details for PubMedCentralID PMC4261059
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Genetic variant in folate homeostasis is associated with lower warfarin dose in African Americans
BLOOD
2014; 124 (14): 2298-2305
Abstract
The anticoagulant warfarin has >30 million prescriptions per year in the United States. Doses can vary 20-fold between patients, and incorrect dosing can result in serious adverse events. Variation in warfarin pharmacokinetic and pharmacodynamic genes, such as CYP2C9 and VKORC1, do not fully explain the dose variability in African Americans. To identify additional genetic contributors to warfarin dose, we exome sequenced 103 African Americans on stable doses of warfarin at extremes (≤ 35 and ≥ 49 mg/week). We found an association between lower warfarin dose and a population-specific regulatory variant, rs7856096 (P = 1.82 × 10(-8), minor allele frequency = 20.4%), in the folate homeostasis gene folylpolyglutamate synthase (FPGS). We replicated this association in an independent cohort of 372 African American subjects whose stable warfarin doses represented the full dosing spectrum (P = .046). In a combined cohort, adding rs7856096 to the International Warfarin Pharmacogenetic Consortium pharmacogenetic dosing algorithm resulted in a 5.8 mg/week (P = 3.93 × 10(-5)) decrease in warfarin dose for each allele carried. The variant overlaps functional elements and was associated (P = .01) with FPGS gene expression in lymphoblastoid cell lines derived from combined HapMap African populations (N = 326). Our results provide the first evidence linking genetic variation in folate homeostasis to warfarin response.
View details for DOI 10.1182/blood-2014-04-568436
View details for Web of Science ID 000342763900023
View details for PubMedCentralID PMC4183989
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Genetic variant in folate homeostasis is associated with lower warfarin dose in African Americans.
Blood
2014; 124 (14): 2298-2305
Abstract
The anticoagulant warfarin has >30 million prescriptions per year in the United States. Doses can vary 20-fold between patients, and incorrect dosing can result in serious adverse events. Variation in warfarin pharmacokinetic and pharmacodynamic genes, such as CYP2C9 and VKORC1, do not fully explain the dose variability in African Americans. To identify additional genetic contributors to warfarin dose, we exome sequenced 103 African Americans on stable doses of warfarin at extremes (≤ 35 and ≥ 49 mg/week). We found an association between lower warfarin dose and a population-specific regulatory variant, rs7856096 (P = 1.82 × 10(-8), minor allele frequency = 20.4%), in the folate homeostasis gene folylpolyglutamate synthase (FPGS). We replicated this association in an independent cohort of 372 African American subjects whose stable warfarin doses represented the full dosing spectrum (P = .046). In a combined cohort, adding rs7856096 to the International Warfarin Pharmacogenetic Consortium pharmacogenetic dosing algorithm resulted in a 5.8 mg/week (P = 3.93 × 10(-5)) decrease in warfarin dose for each allele carried. The variant overlaps functional elements and was associated (P = .01) with FPGS gene expression in lymphoblastoid cell lines derived from combined HapMap African populations (N = 326). Our results provide the first evidence linking genetic variation in folate homeostasis to warfarin response.
View details for DOI 10.1182/blood-2014-04-568436
View details for PubMedID 25079360
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Targeted Exon Capture and Sequencing in Sporadic Amyotrophic Lateral Sclerosis
PLOS GENETICS
2014; 10 (10)
Abstract
Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease that results in progressive degeneration of motor neurons, ultimately leading to paralysis and death. Approximately 10% of ALS cases are familial, with the remaining 90% of cases being sporadic. Genetic studies in familial cases of ALS have been extremely informative in determining the causative mutations behind ALS, especially as the same mutations identified in familial ALS can also cause sporadic disease. However, the cause of ALS in approximately 30% of familial cases and in the majority of sporadic cases remains unknown. Sporadic ALS cases represent an underutilized resource for genetic information about ALS; therefore, we undertook a targeted sequencing approach of 169 known and candidate ALS disease genes in 242 sporadic ALS cases and 129 matched controls to try to identify novel variants linked to ALS. We found a significant enrichment in novel and rare variants in cases versus controls, indicating that we are likely identifying disease associated mutations. This study highlights the utility of next generation sequencing techniques combined with functional studies and rare variant analysis tools to provide insight into the genetic etiology of a heterogeneous sporadic disease.
View details for DOI 10.1371/journal.pgen.1004704
View details for Web of Science ID 000344650700067
View details for PubMedCentralID PMC4191946
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Targeted exon capture and sequencing in sporadic amyotrophic lateral sclerosis.
PLoS genetics
2014; 10 (10)
Abstract
Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease that results in progressive degeneration of motor neurons, ultimately leading to paralysis and death. Approximately 10% of ALS cases are familial, with the remaining 90% of cases being sporadic. Genetic studies in familial cases of ALS have been extremely informative in determining the causative mutations behind ALS, especially as the same mutations identified in familial ALS can also cause sporadic disease. However, the cause of ALS in approximately 30% of familial cases and in the majority of sporadic cases remains unknown. Sporadic ALS cases represent an underutilized resource for genetic information about ALS; therefore, we undertook a targeted sequencing approach of 169 known and candidate ALS disease genes in 242 sporadic ALS cases and 129 matched controls to try to identify novel variants linked to ALS. We found a significant enrichment in novel and rare variants in cases versus controls, indicating that we are likely identifying disease associated mutations. This study highlights the utility of next generation sequencing techniques combined with functional studies and rare variant analysis tools to provide insight into the genetic etiology of a heterogeneous sporadic disease.
View details for DOI 10.1371/journal.pgen.1004704
View details for PubMedID 25299611
View details for PubMedCentralID PMC4191946
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Genotype-Guided Dosing of Vitamin K Antagonists
NEW ENGLAND JOURNAL OF MEDICINE
2014; 370 (18): 1762–63
View details for Web of Science ID 000335405200021
View details for PubMedID 24804303
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Path-scan: a reporting tool for identifying clinically actionable variants.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
2014; 19: 229-240
Abstract
The American College of Medical Genetics and Genomics (ACMG) recently released guidelines regarding the reporting of incidental findings in sequencing data. Given the availability of Direct to Consumer (DTC) genetic testing and the falling cost of whole exome and genome sequencing, individuals will increasingly have the opportunity to analyze their own genomic data. We have developed a web-based tool, PATH-SCAN, which annotates individual genomes and exomes for ClinVar designated pathogenic variants found within the genes from the ACMG guidelines. Because mutations in these genes predispose individuals to conditions with actionable outcomes, our tool will allow individuals or researchers to identify potential risk variants in order to consult physicians or genetic counselors for further evaluation. Moreover, our tool allows individuals to anonymously submit their pathogenic burden, so that we can crowd source the collection of quantitative information regarding the frequency of these variants. We tested our tool on 1092 publicly available genomes from the 1000 Genomes project, 163 genomes from the Personal Genome Project, and 15 genomes from a clinical genome sequencing research project. Excluding the most commonly seen variant in 1000 Genomes, about 20% of all genomes analyzed had a ClinVar designated pathogenic variant that required further evaluation.
View details for PubMedID 24297550
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PATH-SCAN: A REPORTING TOOL FOR IDENTIFYING CLINICALLY ACTIONABLE VARIANTS
WORLD SCIENTIFIC PUBL CO PTE LTD. 2014: 229–40
View details for Web of Science ID 000461865200022
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Genetic variants associated with warfarin dose in African-American individuals: a genome-wide association study.
Lancet
2013; 382 (9894): 790-796
Abstract
BACKGROUND: VKORC1 and CYP2C9 are important contributors to warfarin dose variability, but explain less variability for individuals of African descent than for those of European or Asian descent. We aimed to identify additional variants contributing to warfarin dose requirements in African Americans. METHODS: We did a genome-wide association study of discovery and replication cohorts. Samples from African-American adults (aged ≥18 years) who were taking a stable maintenance dose of warfarin were obtained at International Warfarin Pharmacogenetics Consortium (IWPC) sites and the University of Alabama at Birmingham (Birmingham, AL, USA). Patients enrolled at IWPC sites but who were not used for discovery made up the independent replication cohort. All participants were genotyped. We did a stepwise conditional analysis, conditioning first for VKORC1 -1639G→A, followed by the composite genotype of CYP2C9*2 and CYP2C9*3. We prespecified a genome-wide significance threshold of p<5×10(-8) in the discovery cohort and p<0·0038 in the replication cohort. FINDINGS: The discovery cohort contained 533 participants and the replication cohort 432 participants. After the prespecified conditioning in the discovery cohort, we identified an association between a novel single nucleotide polymorphism in the CYP2C cluster on chromosome 10 (rs12777823) and warfarin dose requirement that reached genome-wide significance (p=1·51×10(-8)). This association was confirmed in the replication cohort (p=5·04×10(-5)); analysis of the two cohorts together produced a p value of 4·5×10(-12). Individuals heterozygous for the rs12777823 A allele need a dose reduction of 6·92 mg/week and those homozygous 9·34 mg/week. Regression analysis showed that the inclusion of rs12777823 significantly improves warfarin dose variability explained by the IWPC dosing algorithm (21% relative improvement). INTERPRETATION: A novel CYP2C single nucleotide polymorphism exerts a clinically relevant effect on warfarin dose in African Americans, independent of CYP2C9*2 and CYP2C9*3. Incorporation of this variant into pharmacogenetic dosing algorithms could improve warfarin dose prediction in this population. FUNDING: National Institutes of Health, American Heart Association, Howard Hughes Medical Institute, Wisconsin Network for Health Research, and the Wellcome Trust.
View details for DOI 10.1016/S0140-6736(13)60681-9
View details for PubMedID 23755828
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Pathway analysis of genome-wide data improves warfarin dose prediction
BMC GENOMICS
2013; 14
Abstract
Many genome-wide association studies focus on associating single loci with target phenotypes. However, in the setting of rare variation, accumulating sufficient samples to assess these associations can be difficult. Moreover, multiple variations in a gene or a set of genes within a pathway may all contribute to the phenotype, suggesting that the aggregation of variations found over the gene or pathway may be useful for improving the power to detect associations.Here, we present a method for aggregating single nucleotide polymorphisms (SNPs) along biologically relevant pathways in order to seek genetic associations with phenotypes. Our method uses all available genetic variants and does not remove those in linkage disequilibrium (LD). Instead, it uses a novel SNP weighting scheme to down-weight the contributions of correlated SNPs. We apply our method to three cohorts of patients taking warfarin: two European descent cohorts and an African American cohort. Although the clinical covariates and key pharmacogenetic loci for warfarin have been characterized, our association metric identifies a significant association with mutations distributed throughout the pathway of warfarin metabolism. We improve dose prediction after using all known clinical covariates and pharmacogenetic variants in VKORC1 and CYP2C9. In particular, we find that at least 1% of the missing heritability in warfarin dose may be due to the aggregated effects of variations in the warfarin metabolic pathway, even though the SNPs do not individually show a significant association.Our method allows researchers to study aggregative SNP effects in an unbiased manner by not preselecting SNPs. It retains all the available information by accounting for LD-structure through weighting, which eliminates the need for LD pruning.
View details for DOI 10.1186/1471-2164-14-S3-S11
View details for Web of Science ID 000319869500011
View details for PubMedID 23819817
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Pathway analysis of genome-wide data improves warfarin dose prediction.
BMC genomics
2013; 14: S11-?
Abstract
Many genome-wide association studies focus on associating single loci with target phenotypes. However, in the setting of rare variation, accumulating sufficient samples to assess these associations can be difficult. Moreover, multiple variations in a gene or a set of genes within a pathway may all contribute to the phenotype, suggesting that the aggregation of variations found over the gene or pathway may be useful for improving the power to detect associations.Here, we present a method for aggregating single nucleotide polymorphisms (SNPs) along biologically relevant pathways in order to seek genetic associations with phenotypes. Our method uses all available genetic variants and does not remove those in linkage disequilibrium (LD). Instead, it uses a novel SNP weighting scheme to down-weight the contributions of correlated SNPs. We apply our method to three cohorts of patients taking warfarin: two European descent cohorts and an African American cohort. Although the clinical covariates and key pharmacogenetic loci for warfarin have been characterized, our association metric identifies a significant association with mutations distributed throughout the pathway of warfarin metabolism. We improve dose prediction after using all known clinical covariates and pharmacogenetic variants in VKORC1 and CYP2C9. In particular, we find that at least 1% of the missing heritability in warfarin dose may be due to the aggregated effects of variations in the warfarin metabolic pathway, even though the SNPs do not individually show a significant association.Our method allows researchers to study aggregative SNP effects in an unbiased manner by not preselecting SNPs. It retains all the available information by accounting for LD-structure through weighting, which eliminates the need for LD pruning.
View details for DOI 10.1186/1471-2164-14-S3-S11
View details for PubMedID 23819817
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Chapter 7: Pharmacogenomics
PLOS COMPUTATIONAL BIOLOGY
2012; 8 (12)
Abstract
There is great variation in drug-response phenotypes, and a "one size fits all" paradigm for drug delivery is flawed. Pharmacogenomics is the study of how human genetic information impacts drug response, and it aims to improve efficacy and reduced side effects. In this article, we provide an overview of pharmacogenetics, including pharmacokinetics (PK), pharmacodynamics (PD), gene and pathway interactions, and off-target effects. We describe methods for discovering genetic factors in drug response, including genome-wide association studies (GWAS), expression analysis, and other methods such as chemoinformatics and natural language processing (NLP). We cover the practical applications of pharmacogenomics both in the pharmaceutical industry and in a clinical setting. In drug discovery, pharmacogenomics can be used to aid lead identification, anticipate adverse events, and assist in drug repurposing efforts. Moreover, pharmacogenomic discoveries show promise as important elements of physician decision support. Finally, we consider the ethical, regulatory, and reimbursement challenges that remain for the clinical implementation of pharmacogenomics.
View details for DOI 10.1371/journal.pcbi.1002817
View details for Web of Science ID 000312901500023
View details for PubMedID 23300409
View details for PubMedCentralID PMC3531317
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Data-Driven Prediction of Drug Effects and Interactions
SCIENCE TRANSLATIONAL MEDICINE
2012; 4 (125)
Abstract
Adverse drug events remain a leading cause of morbidity and mortality around the world. Many adverse events are not detected during clinical trials before a drug receives approval for use in the clinic. Fortunately, as part of postmarketing surveillance, regulatory agencies and other institutions maintain large collections of adverse event reports, and these databases present an opportunity to study drug effects from patient population data. However, confounding factors such as concomitant medications, patient demographics, patient medical histories, and reasons for prescribing a drug often are uncharacterized in spontaneous reporting systems, and these omissions can limit the use of quantitative signal detection methods used in the analysis of such data. Here, we present an adaptive data-driven approach for correcting these factors in cases for which the covariates are unknown or unmeasured and combine this approach with existing methods to improve analyses of drug effects using three test data sets. We also present a comprehensive database of drug effects (Offsides) and a database of drug-drug interaction side effects (Twosides). To demonstrate the biological use of these new resources, we used them to identify drug targets, predict drug indications, and discover drug class interactions. We then corroborated 47 (P < 0.0001) of the drug class interactions using an independent analysis of electronic medical records. Our analysis suggests that combined treatment with selective serotonin reuptake inhibitors and thiazides is associated with significantly increased incidence of prolonged QT intervals. We conclude that confounding effects from covariates in observational clinical data can be controlled in data analyses and thus improve the detection and prediction of adverse drug effects and interactions.
View details for DOI 10.1126/scitranslmed.3003377
View details for Web of Science ID 000301538300005
View details for PubMedID 22422992
View details for PubMedCentralID PMC3382018
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Bioinformatics challenges for personalized medicine
BIOINFORMATICS
2011; 27 (13): 1741-1748
Abstract
Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine. New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice.russ.altman@stanford.edu
View details for DOI 10.1093/bioinformatics/btr295
View details for Web of Science ID 000291752600050
View details for PubMedID 21596790
View details for PubMedCentralID PMC3117361