Alan Karthikesalingam
Alan is a clinician and Research Scientist working on Foundation Models for health, most recently including Med-PaLM, Med-PaLM-2, Med-PaLM-Multimodal and AMIE. Prior to this his work at DeepMind and Google explored applications of AI in radiology, ophthalmology, dermatology and electronic health records, resulting in papers published in Nature and Nature Medicine. He is an honorary Lecturer in Vascular Surgery at Imperial College in London. He completed his MA in Neuroscience and Medical Degree (MBBChir) at the University of Cambridge before specialist training in surgery in the London Deanery, where he completed his Membership of the Royal College of Surgeons (MRCS), PhD in Vascular Surgery and was appointed as a NIHR Clinical Lecturer. In 2017 he joined DeepMind's health research team and in 2019 joined Google Health. Prior to joining Google he had published over 150 peer-reviewed articles including first-author studies in the New England Journal of Medicine and The Lancet.
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Crowdsourcing Dermatology Images with Google Search Ads: Creating a Diverse and Representative Dataset of Real-World Skin Conditions
Abbi Ward
Ashley Carrick
Dawn Siegel
Jay Hartford
Jimmy Li
Julie Wang
Justin Ko
Pradeep Kumar S
Renee Wong
Sriram Lakshminarasimhan
Steven Lin
Sunny Virmani
arXiv(2024)
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Background
Health datasets from clinical sources do not reflect the breadth and diversity of disease in the real world, impacting research, medical education and artificial intelligence (AI) tool development. Dermatology is a suitable area to develop and test a new and scalable method to create representative health datasets.
Methods
We used Google Search advertisements to solicit contributions of images of dermatology conditions, demographic and symptom information from internet users in the United States (US) over 265 days starting March 2023. With informed contributor consent, we described and released this dataset containing 10,106 images from 5058 contributions, with dermatologist labels as well as Fitzpatrick Skin Type and Monk Skin Tone labels for the images.
Results
We received 22 ± 14 submissions/day over 265 days. Female contributors (66.04%) and younger individuals (52.3% < age 40) had a higher representation in the dataset compared to the US population, and 36.6% of contributors had a non-White racial or ethnic identity. Over 97.5% of contributions were genuine images of skin conditions. Image quality had no impact on dermatologist confidence in assigning a differential diagnosis. The dataset consists largely of short duration (54% with onset < 7 days ago) allergic, infectious, and inflammatory conditions. Fitzpatrick skin type distribution is well-balanced, considering the geographical origin of the dataset and the absence of enrichment for population groups or skin tones.
Interpretation
Search ads are effective at crowdsourcing images of health conditions. The SCIN dataset bridges important gaps in the availability of representative images of common skin conditions.
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A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
Heather Cole-Lewis
Nenad Tomašev
Liam McCoy
Leo Anthony Celi
Alanna Walton
Akeiylah DeWitt
Philip Mansfield
Sushant Prakash
Joelle Barral
Ivor Horn
Karan Singhal
Arxiv(2024) (to appear)
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Large language models (LLMs) hold immense promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities. Reliably evaluating equity-related model failures is a critical step toward developing systems that promote health equity. In this work, we present resources and methodologies for surfacing biases with potential to precipitate equity-related harms in long-form, LLM-generated answers to medical questions and then conduct an empirical case study with Med-PaLM 2, resulting in the largest human evaluation study in this area to date. Our contributions include a multifactorial framework for human assessment of LLM-generated answers for biases, and EquityMedQA, a collection of seven newly-released datasets comprising both manually-curated and LLM-generated questions enriched for adversarial queries. Both our human assessment framework and dataset design process are grounded in an iterative participatory approach and review of possible biases in Med-PaLM 2 answers to adversarial queries. Through our empirical study, we find that the use of a collection of datasets curated through a variety of methodologies, coupled with a thorough evaluation protocol that leverages multiple assessment rubric designs and diverse rater groups, surfaces biases that may be missed via narrower evaluation approaches. Our experience underscores the importance of using diverse assessment methodologies and involving raters of varying backgrounds and expertise. We emphasize that while our framework can identify specific forms of bias, it is not sufficient to holistically assess whether the deployment of an AI system promotes equitable health outcomes. We hope the broader community leverages and builds on these tools and methods towards realizing a shared goal of LLMs that promote accessible and equitable healthcare for all.
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Conversational AI in health: Design considerations from a Wizard-of-Oz dermatology case study with users, clinicians and a medical LLM
Brenna Li
Amy Wang
Patricia Strachan
Julie Anne Seguin
Sami Lachgar
Karyn Schroeder
Renee Wong
Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, pp. 10
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Although skin concerns are common, access to specialist care is limited. Artificial intelligence (AI)-assisted tools to support medical decisions may provide patients with feedback on their concerns while also helping ensure the most urgent cases are routed to dermatologists. Although AI-based conversational agents have been explored recently, how they are perceived by patients and clinicians is not well understood. We conducted a Wizard-of-Oz study involving 18 participants with real skin concerns. Participants were randomly assigned to interact with either a clinician agent (portrayed by a dermatologist) or an LLM agent (supervised by a dermatologist) via synchronous multimodal chat. In both conditions, participants found the conversation to be helpful in understanding their medical situation and alleviate their concerns. Through qualitative coding of the conversation transcripts, we provide insight on the importance of empathy and effective information-seeking. We conclude with design considerations for future AI-based conversational agents in healthcare settings.
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Towards Conversational Diagnostic AI
Anil Palepu
Khaled Saab
Jan Freyberg
Ryutaro Tanno
Amy Wang
Brenna Li
Nenad Tomašev
Karan Singhal
Le Hou
Albert Webson
Kavita Kulkarni
Sara Mahdavi
Juro Gottweis
Joelle Barral
Kat Chou
Arxiv(2024) (to appear)
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At the heart of medicine lies the physician-patient dialogue, where skillful history-taking paves the way for accurate diagnosis, effective management, and enduring trust. Artificial Intelligence (AI) systems capable of diagnostic dialogue could increase accessibility, consistency, and quality of care. However, approximating clinicians' expertise is an outstanding grand challenge. Here, we introduce AMIE (Articulate Medical Intelligence Explorer), a Large Language Model (LLM) based AI system optimized for diagnostic dialogue.
AMIE uses a novel self-play based simulated environment with automated feedback mechanisms for scaling learning across diverse disease conditions, specialties, and contexts. We designed a framework for evaluating clinically-meaningful axes of performance including history-taking, diagnostic accuracy, management reasoning, communication skills, and empathy. We compared AMIE's performance to that of primary care physicians (PCPs) in a randomized, double-blind crossover study of text-based consultations with validated patient actors in the style of an Objective Structured Clinical Examination (OSCE). The study included 149 case scenarios from clinical providers in Canada, the UK, and India, 20 PCPs for comparison with AMIE, and evaluations by specialist physicians and patient actors. AMIE demonstrated greater diagnostic accuracy and superior performance on 28 of 32 axes according to specialist physicians and 24 of 26 axes according to patient actors. Our research has several limitations and should be interpreted with appropriate caution. Clinicians were limited to unfamiliar synchronous text-chat which permits large-scale LLM-patient interactions but is not representative of usual clinical practice. While further research is required before AMIE could be translated to real-world settings, the results represent a milestone towards conversational diagnostic AI.
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Understanding metric-related pitfalls in image analysis validation
Annika Reinke
Lena Maier-Hein
Paul Jager
Shravya Shetty
Understanding Metrics Workgroup
Nature Methods (2024)
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Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.
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Quantifying urban park use in the USA at scale: empirical estimates of realised park usage using smartphone location data
Michael T Young
Swapnil Vispute
Stylianos Serghiou
Akim Kumok
Yash Shah
Kevin J. Lane
Flannery Black-Ingersoll
Paige Brochu
Monica Bharel
Sarah Skenazy
Shailesh Bavadekar
Mansi Kansal
Evgeniy Gabrilovich
Gregory A. Wellenius
Lancet Planetary Health(2024)
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Summary
Background A large body of evidence connects access to greenspace with substantial benefits to physical and mental
health. In urban settings where access to greenspace can be limited, park access and use have been associated with
higher levels of physical activity, improved physical health, and lower levels of markers of mental distress. Despite the
potential health benefits of urban parks, little is known about how park usage varies across locations (between or
within cities) or over time.
Methods We estimated park usage among urban residents (identified as residents of urban census tracts) in
498 US cities from 2019 to 2021 from aggregated and anonymised opted-in smartphone location history data. We
used descriptive statistics to quantify differences in park usage over time, between cities, and across census tracts
within cities, and used generalised linear models to estimate the associations between park usage and census tract
level descriptors.
Findings In spring (March 1 to May 31) 2019, 18·9% of urban residents visited a park at least once per week, with
average use higher in northwest and southwest USA, and lowest in the southeast. Park usage varied substantially
both within and between cities; was unequally distributed across census tract-level markers of race, ethnicity, income,
and social vulnerability; and was only moderately correlated with established markers of census tract greenspace. In
spring 2019, a doubling of walking time to parks was associated with a 10·1% (95% CI 5·6–14·3) lower average
weekly park usage, adjusting for city and social vulnerability index. The median decline in park usage from spring
2019 to spring 2020 was 38·0% (IQR 28·4–46·5), coincident with the onset of physical distancing policies across
much of the country. We estimated that the COVID-19-related decline in park usage was more pronounced for those
living further from a park and those living in areas of higher social vulnerability.
Interpretation These estimates provide novel insights into the patterns and correlates of park use and could enable
new studies of the health benefits of urban greenspace. In addition, the availability of an empirical park usage metric
that varies over time could be a useful tool for assessing the effectiveness of policies intended to increase such
activities.
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An intentional approach to managing bias in embedding models
Atilla P. Kiraly
Jungyeon Park
Rory Pilgrim
Charles Lau
Heather Cole-Lewis
Shravya Shetty
Krish Eswaran
Leo Anthony Celi
The Lancet Digital Health, 6(2024), E126-E130
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Advances in machine learning for health care have brought concerns about bias from the research community; specifically, the introduction, perpetuation, or exacerbation of care disparities. Reinforcing these concerns is the finding that medical images often reveal signals about sensitive attributes in ways that are hard to pinpoint by both algorithms and people. This finding raises a question about how to best design general purpose pretrained embeddings (GPPEs, defined as embeddings meant to support a broad array of use cases) for building downstream models that are free from particular types of bias. The downstream model should be carefully evaluated for bias, and audited and improved as appropriate. However, in our view, well intentioned attempts to prevent the upstream components—GPPEs—from learning sensitive attributes can have unintended consequences on the downstream models. Despite producing a veneer of technical neutrality, the resultant end-to-end system might still be biased or poorly performing. We present reasons, by building on previously published data, to support the reasoning that GPPEs should ideally contain as much information as the original data contain, and highlight the perils of trying to remove sensitive attributes from a GPPE. We also emphasise that downstream prediction models trained for specific tasks and settings, whether developed using GPPEs or not, should be carefully designed and evaluated to avoid bias that makes models vulnerable to issues such as distributional shift. These evaluations should be done by a diverse team, including social scientists, on a diverse cohort representing the full breadth of the patient population for which the final model is intended.
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Generative models improve fairness of medical classifiers under distribution shifts
Ira Ktena
Olivia Wiles
Isabela Albuquerque
Sylvestre-Alvise Rebuffi
Ryutaro Tanno
Danielle Belgrave
Taylan Cemgil
Nature Medicine(2024)
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Domain generalization is a ubiquitous challenge for machine learning in healthcare. Model performance in real-world conditions might be lower than expected because of discrepancies between the data encountered during deployment and development. Underrepresentation of some groups or conditions during model development is a common cause of this phenomenon. This challenge is often not readily addressed by targeted data acquisition and ‘labeling’ by expert clinicians, which can be prohibitively expensive or practically impossible because of the rarity of conditions or the available clinical expertise. We hypothesize that advances in generative artificial intelligence can help mitigate this unmet need in a steerable fashion, enriching our training dataset with synthetic examples that address shortfalls of underrepresented conditions or subgroups. We show that diffusion models can automatically learn realistic augmentations from data in a label-efficient manner. We demonstrate that learned augmentations make models more robust and statistically fair in-distribution and out of distribution. To evaluate the generality of our approach, we studied three distinct medical imaging contexts of varying difficulty: (1) histopathology, (2) chest X-ray and (3) dermatology images. Complementing real samples with synthetic ones improved the robustness of models in all three medical tasks and increased fairness by improving the accuracy of clinical diagnosis within underrepresented groups, especially out of distribution.
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Towards Generalist Biomedical AI
Danny Driess
Andrew Carroll
Chuck Lau
Ryutaro Tanno
Ira Ktena
Anil Palepu
Basil Mustafa
Aakanksha Chowdhery
Simon Kornblith
Philip Mansfield
Sushant Prakash
Renee Wong
Sunny Virmani
Sara Mahdavi
Bradley Green
Ewa Dominowska
Joelle Barral
Karan Singhal
Pete Florence
NEJM AI(2024)
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BACKGROUND: Medicine is inherently multimodal, requiring the simultaneous interpretation and integration of insights between many data modalities spanning text, imaging, genomics, and more. Generalist biomedical artificial intelligence systems that flexibly encode, integrate, and interpret these data might better enable impactful applications ranging from scientific discovery to care delivery.
METHODS: To catalyze development of these models, we curated MultiMedBench, a new multimodal biomedical benchmark. MultiMedBench encompasses 14 diverse tasks, such as medical question answering, mammography and dermatology image interpretation, radiology report generation and summarization, and genomic variant calling. We then introduced Med-PaLM Multimodal (Med-PaLM M), our proof of concept for a generalist biomedical AI system that flexibly encodes and interprets biomedical data including clinical language, imaging, and genomics with the same set of model weights. To further probe the capabilities and limitations of Med-PaLM M, we conducted a radiologist evaluation of model-generated (and human) chest x-ray reports.
RESULTS: We observed encouraging performance across model scales. Med-PaLM M reached performance competitive with or exceeding the state of the art on all MultiMedBench tasks, often surpassing specialist models by a wide margin. In a side-by-side ranking on 246 retrospective chest x-rays, clinicians expressed a pairwise preference for Med-PaLM Multimodal reports over those produced by radiologists in up to 40.50% of cases, suggesting potential clinical utility.
CONCLUSIONS: Although considerable work is needed to validate these models in real-world cases and understand if cross-modality generalization is possible, our results represent a milestone toward the development of generalist biomedical artificial intelligence systems.
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Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging
Laura Anne Culp
Jan Freyberg
Basil Mustafa
Sebastien Baur
Simon Kornblith
Ting Chen
Patricia MacWilliams
Sara Mahdavi
Megan Zoë Walker
Aaron Loh
Cameron Chen
Scott Mayer McKinney
Zach William Beaver
Fiona Keleher Ryan
Mozziyar Etemadi
Umesh Telang
Lily Hao Yi Peng
Geoffrey Everest Hinton
Neil Houlsby
Mohammad Norouzi
Nature Biomedical Engineering(2023)
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Machine-learning models for medical tasks can match or surpass the performance of clinical experts. However, in settings differing from those of the training dataset, the performance of a model can deteriorate substantially. Here we report a representation-learning strategy for machine-learning models applied to medical-imaging tasks that mitigates such ‘out of distribution’ performance problem and that improves model robustness and training efficiency. The strategy, which we named REMEDIS (for ‘Robust and Efficient Medical Imaging with Self-supervision’), combines large-scale supervised transfer learning on natural images and intermediate contrastive self-supervised learning on medical images and requires minimal task-specific customization. We show the utility of REMEDIS in a range of diagnostic-imaging tasks covering six imaging domains and 15 test datasets, and by simulating three realistic out-of-distribution scenarios. REMEDIS improved in-distribution diagnostic accuracies up to 11.5% with respect to strong supervised baseline models, and in out-of-distribution settings required only 1–33% of the data for retraining to match the performance of supervised models retrained using all available data. REMEDIS may accelerate the development lifecycle of machine-learning models for medical imaging.
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