Shekoofeh Azizi
I am a staff research scientist and a research lead at Google DeepMind. My research is focused on creating a new paradigm of biomedical super-intelligence to accelerate scientific discovery, with a particular focus on cancer. To this end, my team has been pioneering the application of the LLMs in new scientific frontiers including therapeutics and single-cell biology such as TxGemma, Tx-LLM, and C2S-Scale. I am also one of the research leads driving the ambitious effort behind the creation Med-PaLM series and Med-Gemini which are Google's flagship LLMs, meticulously designed for medical applications.
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Scaling Large Language Models For Next-Generation Single-Cell Analysis
Syed Asad Rizvi
Daniel Levine
Aakash Patel
Shiyang Zhang
Eric Wang
Curtis Jamison Perry
Nicole Mayerli Constante
Sizhuang He
David Zhang
Cerise Tang
Zhuoyang Lyu
Rayyan Darji
Chang Li
Emily Sun
David Jeong
Lawrence Zhao
Jennifer Kwan
David Braun
Brian Hafler
Hattie Chung
Rahul M. Dhodapkar
Paul Jaeger
Jeffrey Ishizuka
David van Dijk
biorxiv (2025)
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Single-cell RNA sequencing has transformed our understanding of cellular diversity, yet current singlecell foundation models (scFMs) remain limited in their scalability, flexibility across diverse tasks, and ability to natively integrate textual information. In this work, we build upon the Cell2Sentence (C2S) framework, which represents scRNA-seq profiles as textual “cell sentences,” to train Large Language Models (LLMs) on a corpus comprising over one billion tokens of transcriptomic data, biological text, and metadata. Scaling the model to 27 billion parameters yields consistent improvements in predictive and generative capabilities and supports advanced downstream tasks that require synthesis of information across multi-cellular contexts. Targeted fine-tuning with modern reinforcement learning techniques produces strong performance in perturbation response prediction, natural language interpretation, and complex biological reasoning. This predictive strength directly enabled a dualcontext virtual screen that uncovered a striking context split for the kinase inhibitor silmitasertib (CX-4945), suggesting its potential as a synergistic, interferon-conditional amplifier of antigen presentation. Experimental validation in human cell models unseen during training confirmed this hypothesis, demonstrating that C2S-Scale can generate biologically grounded, testable discoveries of context-conditioned biology. C2S-Scale unifies transcriptomic and textual data at unprecedented scales, surpassing both specialized single-cell models and general-purpose LLMs to provide a platform for next-generation single-cell analysis and the development of “virtual cells.”
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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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Health AI Developer Foundations
Atilla Kiraly
Sebastien Baur
Kenneth Philbrick
Fereshteh Mahvar
Liron Yatziv
Tiffany Chen
Bram Sterling
Nick George
Fayaz Jamil
Jing Tang
Kai Bailey
Faruk Ahmed
Akshay Goel
Abbi Ward
Lin Yang
Shravya Shetty
Daniel Golden
Tim Thelin
Rory Pilgrim
Can "John" Kirmizi
arXiv (2024)
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Robust medical Machine Learning (ML) models have the potential to revolutionize healthcare by accelerating clinical research, improving workflows and outcomes, and producing novel insights or capabilities. Developing such ML models from scratch is cost prohibitive and requires substantial compute, data, and time (e.g., expert labeling). To address these challenges, we introduce Health AI Developer Foundations (HAI-DEF), a suite of pre-trained, domain-specific foundation models, tools, and recipes to accelerate building ML for health applications. The models cover various modalities and domains, including radiology (X-rays and computed tomography), histopathology, dermatological imaging, and audio. These models provide domain specific embeddings that facilitate AI development with less labeled data, shorter training times, and reduced computational costs compared to traditional approaches. In addition, we utilize a common interface and style across these models, and prioritize usability to enable developers to integrate HAI-DEF efficiently. We present model evaluations across various tasks and conclude with a discussion of their application and evaluation, covering the importance of ensuring efficacy, fairness, and equity. Finally, while HAI-DEF and specifically the foundation models lower the barrier to entry for ML in healthcare, we emphasize the importance of validation with problem- and population-specific data for each desired usage setting. This technical report will be updated over time as more modalities and features are added.
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A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models
Heather Cole-Lewis
Awa Dieng
Nenad Tomašev
Liam McCoy
Leo Anthony Celi
Alanna Walton
Chirag Nagpal
Akeiylah DeWitt
Philip Mansfield
Sushant Prakash
Joelle Barral
Ivor Horn
Karan Singhal
Nature Medicine (2024)
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Large language models (LLMs) hold 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. 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 conduct a large-scale empirical case study with the Med-PaLM 2 LLM. Our contributions include a multifactorial framework for human assessment of LLM-generated answers for biases and EquityMedQA, a collection of seven datasets enriched for adversarial queries. Both our human assessment framework and our dataset design process are grounded in an iterative participatory approach and review of Med-PaLM 2 answers. Through our empirical study, we find that our approach surfaces biases that may be missed by narrower evaluation approaches. Our experience underscores the importance of using diverse assessment methodologies and involving raters of varying backgrounds and expertise. While our approach is not sufficient to holistically assess whether the deployment of an artificial intelligence (AI) system promotes equitable health outcomes, we hope that it can be leveraged and built upon toward a shared goal of LLMs that promote accessible and equitable healthcare.
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Towards Conversational Diagnostic AI
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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Towards Generalist Biomedical AI
Danny Driess
Andrew Carroll
Chuck Lau
Ryutaro Tanno
Ira Ktena
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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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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Predicting lymph node metastasis from primary tumor histology and clinicopathologic factors in colorectal cancer using deep learning
Justin Krogue
Fraser Tan
Isabelle Flament-Auvigne
Trissia Brown
Markus Plass
Robert Reihs
Heimo Mueller
Kurt Zatloukal
Pema Richeson
Lily Peng
Craig Mermel
Cameron Chen
Saurabh Gombar
Thomas Montine
Jeanne Shen
Nature Communications Medicine, 3 (2023), pp. 59
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Background: Presence of lymph node metastasis (LNM) influences prognosis and clinical decision-making in colorectal cancer. However, detection of LNM is variable and depends on a number of external factors. Deep learning has shown success in computational pathology, but has struggled to boost performance when combined with known predictors.
Methods: Machine-learned features are created by clustering deep learning embeddings of small patches of tumor in colorectal cancer via k-means, and then selecting the top clusters that add predictive value to a logistic regression model when combined with known baseline clinicopathological variables. We then analyze performance of logistic regression models trained with and without these machine-learned features in combination with the baseline variables.
Results: The machine-learned extracted features provide independent signal for the presence of LNM (AUROC: 0.638, 95% CI: [0.590, 0.683]). Furthermore, the machine-learned features add predictive value to the set of 6 clinicopathologic variables in an external validation set (likelihood ratio test, p < 0.00032; AUROC: 0.740, 95% CI: [0.701, 0.780]). A model incorporating these features can also further risk-stratify patients with and without identified metastasis (p < 0.001 for both stage II and stage III).
Conclusion: This work demonstrates an effective approach to combine deep learning with established clinicopathologic factors in order to identify independently informative features associated with LNM. Further work building on these specific results may have important impact in prognostication and therapeutic decision making for LNM. Additionally, this general computational approach may prove useful in other contexts.
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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
Scott Mayer McKinney
Jim Winkens
Zach William Beaver
Fiona Keleher Ryan
Justin David Krogue
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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Enhancing diagnostic accuracy of medical AI systems via selective deferral to clinicians
Dj Dvijotham
Jim Winkens
Melih Barsbey
Sumedh Ghaisas
Robert Stanforth
Nick Pawlowski
Patricia Strachan
Zahra Ahmed
Yoram Bachrach
Laura Culp
Mayank Daswani
Jan Freyberg
Christopher Kelly
Atilla Kiraly
Timo Kohlberger
Scott Mayer McKinney
Basil Mustafa
Krzysztof Geras
Jan Witowski
Zhi Zhen Qin
Jacob Creswell
Shravya Shetty
Terry Spitz
Taylan Cemgil
Nature Medicine (2023)
Preview abstract
AI systems trained using deep learning have been shown to achieve expert-level identification of diseases in multiple medical imaging settings1,2. While these results are impressive, they don’t accurately reflect the impact of deployment of such systems in a clinical context. Due to the safety-critical nature of this domain and the fact that AI systems are not perfect and can make inaccurate assessments, they are predominantly deployed as assistive tools for clinical experts3. Although clinicians routinely discuss the diagnostic nuances of medical images with each other, weighing human diagnostic confidence against that of an AI system remains a major unsolved barrier to collaborative decision-making4. Furthermore, it has been observed that diagnostic AI models have complementary strengths and weaknesses compared to clinical experts. Yet, complementarity and the assessment of relative confidence between the members of a diagnostic team has remained largely unexploited in how AI systems are currently used in medical settings5.
In this paper, we study the behavior of a team composed of diagnostic AI model(s) and clinician(s) in diagnosing disease. To go beyond the performance level of a standalone AI system, we develop a novel selective deferral algorithm that can learn to decide when to rely on a diagnostic AI model and when to defer to a clinical expert. Using this algorithm, we demonstrate that the composite AI+human system has enhanced accuracy (both sensitivity and specificity) relative to a human-only or an AI-only baseline. We decouple the development of the deferral AI model from training of the underlying diagnostic AI model(s). Development of the deferral AI model only requires i) the predictions of a model(s) on a tuning set of medical images (separate from the diagnostic AI models’ training data), ii) the diagnoses made by clinicians on these images and iii) the ground truth disease labels corresponding to those images.
Our extensive analysis shows that the selective deferral (SD) system exceeds the performance of either clinicians or AI alone in multiple clinical settings: breast and lung cancer screening. For breast cancer screening, double-reading with arbitration (two readers interpreting each mammogram invoking an arbitrator if needed) is a “gold standard” for performance, never previously exceeded using AI6. The SD system exceeds the accuracy of double-reading with arbitration in a large representative UK screening program (25% reduction in false positives despite equivalent true-positive detection and 66% reduction in the requirement for clinicians to read an image), as well as exceeding the performance of a standalone state-of-art AI system (40% reduction in false positives with equivalent detection of true positives). In a large US dataset the SD system exceeds the accuracy of single-reading by board-certified radiologists and a standalone state-of-art AI system (32% reduction in false positives despite equivalent detection of true positives and 55% reduction in the clinician workload required). The SD system further outperforms both clinical experts alone, and AI alone for the detection of lung cancer in low-dose Computed Tomography images from a large national screening study, with 11% reduction in false positives while maintaining sensitivity given 93% reduction in clinician workload required. Furthermore, the SD system allows controllable trade-offs between sensitivity and specificity and can be tuned to target either specificity or sensitivity as desired for a particular clinical application, or a combination of both.
The system generalizes to multiple distribution shifts, retaining superiority to both the AI system alone and human experts alone. We demonstrate that the SD system retains performance gains even on clinicians not present in the training data for the deferral AI. Furthermore, we test the SD system on a new population where the standalone AI system’s performance significantly degrades. We showcase the few-shot adaptation capability of the SD system by demonstrating that the SD system can obtain superiority to both the standalone AI system and the clinician on the new population after being trained on only 40 cases from the new population.
Our comprehensive assessment demonstrates that a selective deferral system could significantly improve clinical outcomes in multiple medical imaging applications, paving the way for higher performance clinical AI systems that can leverage the complementarity between clinical experts and medical AI tools.
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