Vivek Natarajan
Vivek Natarajan is a Research Scientist at Google DeepMind leading research at the intersection of AI, science and medicine.
In particular, Vivek was the lead researcher behind Med-PaLM and Med-PaLM 2, which were the first AI systems to obtain passing and expert level scores on US Medical License exam questions respectively. Med-PaLM was published in Nature in 2023 and has been featured in The Scientific American, Wall Street Journal, The Economist, STAT News, CNBC, Forbes, New Scientist among others.
Vivek co-leads Project AMIE with Dr Alan Karthikesalingam at Google, a research effort aiming to build and democratize conversational, multimodal, diagnostic and empathetic medical super intelligence. Two papers from Project AMIE were recently published in Nature with the AI system surpassing primary care physicians in performing medical consultations in simulated settings.
Finally, Vivek recently co-led the development of the AI co-scientist, a system designed to be a virtual AI collaborator for scientists.
Outside of Google, Vivek is also part of the faculty for executive education at Harvard T.H. Chan School of Public Health in a part-time capacity.
Authored Publications
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A prospective clinical feasibility study of a conversational diagnostic AI in an ambulatory primary care clinic
Peter Brodeur
Jacob M. Koshy
Khaled Saab
Ava Homiar
Roma Ruparel
Charles Wu
Ryutaro Tanno
Joseph Xu
Amy Wang
David Stutz
Hannah M. Ferrera
David Barrett
Lindsey Crowley
Jihyeon Lee
Spencer E. Rittner
Selena K. Zhang
Elahe Vedadi
Christine G. Kohn
Kavita Kulkarni
Vinay Kadiyala
Sara Mahdavi
Wendy Du
David Feinbloom
Renee Wong
Petar Sirkovic
Alessio Orlandi
Juro Gottweis
Joelle Barral
Kat Chou
James Manyika
Rob Fields
Jonathan X. Li
Marc L. Cohen
Adam Rodman
arXiv (2026)
Preview abstract
Large language model (LLM)-based AI systems have shown promise for patient-facing diagnostic and management conversations in simulated settings. Translating these systems into clinical practice requires assessment in real-world workflows with rigorous safety oversight. We report a prospective, single-arm feasibility study of an LLM-based conversational AI, the Articulate Medical Intelligence Explorer (AMIE), conducting clinical history taking and presentation of potential diagnoses for patients to discuss with their provider at urgent care appointments at a leading academic medical center. 100 adult patients completed an AMIE text-chat interaction up to 5 days before their appointment. We sought to assess the conversational safety and quality, patient and clinician experience, and clinical reasoning capabilities compared to primary care providers (PCPs). Human safety supervisors monitored all patient-AMIE interactions in real time and did not need to intervene to stop any consultations based on pre-defined criteria. Patients reported high satisfaction and their attitudes towards AI improved after interacting with AMIE (p < 0.001). PCPs found AMIE's output useful with a positive impact on preparedness. AMIE's differential diagnosis (DDx) included the final diagnosis, per chart review 8 weeks post-encounter, in 90% of cases, with 75% top-3 accuracy. Blinded assessment of AMIE and PCP DDx and management (Mx) plans suggested similar overall DDx and Mx plan quality, without significant differences for DDx (p = 0.6) and appropriateness and safety of Mx (p = 0.1 and 1.0, respectively). PCPs outperformed AMIE in the practicality (p = 0.003) and cost effectiveness (p = 0.004) of Mx. While further research is needed, this study demonstrates the initial feasibility, safety, and user acceptance of conversational AI in a real-world setting, representing crucial steps towards clinical translation.
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CoDaS: AI Co-Data-Scientist for Biomarker Discovery via Wearable Sensors
Juro Gottweis
CJ Park
Salman Rahman
Ahmed Metwally
Hong Yu
Ivor Rendulic
Yuzhe Yang
Petar Sirkovic
Daniel McDuff
Shwetak Patel
Nicolas Stroppa
Yubin Kim
Mark Malhotra
Orson Xu
Sam Schmidgall
Tim Althoff
Elahe Vedadi
Cynthia Breazeal
Hae Won Park
(2026)
Towards conversational artificial intelligence for disease management
Khaled Saab
David Stutz
Yong Cheng
Kavita Kulkarni
Sara Mahdavi
Joelle Barral
James Manyika
Ryutaro Tanno
Adam Rodman
Nature (2026)
Preview abstract
Although large language models have shown promise in diagnostic dialogue, their capabilities for effective management reasoning, including disease progression, therapeutic response and safe medication prescription, have remained underexplored. We have advanced the previously demonstrated diagnostic capabilities of the Articulate Medical Intelligence Explorer (AMIE) using a new large-language-model-based agentic system optimized for multivisit clinical management and dialogue. To ground the reasoning of AMIE in authoritative clinical knowledge, we leveraged the long-context capabilities of Gemini, combining in-context retrieval with structured reasoning to align its output with up-to-date clinical practice guidelines and drug formularies. In a randomized, blinded virtual Objective Structured Clinical Examination study, AMIE was compared to 21 primary care physicians (PCPs) across 100 multivisit case scenarios designed to reflect the guidance of the UK National Institute for Health and Care Excellence and BMJ Best Practice guidelines. AMIE was non-inferior to PCPs in management reasoning, as assessed by specialists, and scored better both with respect to preciseness of treatment and investigation, and in terms of its alignment with and grounding in clinical guidelines. To benchmark medication reasoning, we developed RxQA, a multiple-choice question benchmark that was derived from two national drug formularies (from the USA and UK) and validated by board-certified pharmacists. Although AMIE and PCPs both benefited from the ability to access external drug information, AMIE outperformed PCPs on higher-difficulty questions. Although further research will be needed before real-world translation of AMIE, its strong performance across evaluations marks a significant step towards use of conversational artificial intelligence as a tool in disease management.
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A large language model for complex cardiology care
Jack W O’Sullivan
Khaled Saab
Daniel K. Amponsah
Evaline Cheng
Yong Cheng
Emily Chu
Yaanik Desai
Aly Elezaby
Muhammad Fazal
Tasmeen Hussain
Sneha S. Jain
Daniel Seung Kim
Roy Lan
Jiwen Li
Wilson Tang
Natalie Tapaskar
Victoria Parikh
Ryan Sandoval
Gabriela Spencer-Bonilla
Bryan Wu
Kavita Kulkarni
Philip Mansfield
Juro Gottweis
Joelle Barral
Ryutaro Tanno
Sara Mahdavi
Euan Ashley
Nature Medicine (2026)
Preview abstract
The scarcity of subspecialist medical expertise poses a considerable challenge for healthcare delivery. This issue is particularly acute in cardiology, where timely, accurate management determines outcomes. We explored the potential of Articulate Medical Intelligence Explorer (AMIE), a large language model-based experimental medical artificial intelligence system, to augment clinical decision-making in this challenging context. We conducted a randomized controlled trial comparing large language model-assisted care with the usual care of complex patients suspected of having a genetic cardiomyopathy, and we curated a real-world dataset of complex cases from a subspecialist cardiology practice. Nine participating general cardiologists were provided with access to both clinical text reports and raw diagnostic data—including electrocardiograms, echocardiograms, cardiac magnetic resonance imaging scans and cardiopulmonary exercise testing—and were randomized to manage these cases, either with or without assistance from AMIE. We developed a ten-domain evaluation rubric used by three blinded subspecialists to evaluate the quality of triage, diagnosis and management. In our randomized controlled trial with retrospective patient data, subspecialists favored large language model-assisted responses overall, and for the management plan and diagnostic testing domains, with the remaining domains considered a tie. Overall, subspecialists preferred AMIE-assisted cardiology assessments 46.7% of the time, compared with 32.7% for cardiologists alone (P = 0.02), with 20.6% rated as a tie. Subspecialists also quantified errors, extra and missing content, reasoning and potential bias. Cardiologists alone had more clinically significant errors (24.3% versus 13.1%, P = 0.033) and more missing content (37.4% versus 17.8%, P = 0.0021) than cardiologists assisted by AMIE. Lastly, cardiologists who used AMIE reported that AMIE helped their assessment more than half the time (57.0%) and saved time in 50.5% of cases.
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Towards conversational diagnostic artificial intelligence
Khaled Saab
Jan Freyberg
Ryutaro Tanno
Amy Wang
Brenna Li
Nenad Tomašev
Karan Singhal
Yong Cheng
Le Hou
Albert Webson
Kavita Kulkarni
Sara Mahdavi
Juro Gottweis
Joelle Barral
Kat Chou
Nature (2025)
Preview abstract
At the heart of medicine lies physician–patient dialogue, where skillful history-taking enables effective diagnosis, management and enduring trust. Artificial intelligence (AI) systems capable of diagnostic dialogue could increase accessibility and quality of care. However, approximating clinicians’ expertise is an outstanding challenge. Here we introduce AMIE (Articulate Medical Intelligence Explorer), a large language model (LLM)-based AI system optimized for diagnostic dialogue. AMIE uses a self-play-based simulated environment with automated feedback for scaling learning across disease conditions, specialties and contexts. We designed a framework for evaluating clinically meaningful axes of performance, including history-taking, diagnostic accuracy, management, communication skills and empathy. We compared AMIE’s performance to that of primary care physicians in a randomized, double-blind crossover study of text-based consultations with validated patient-actors similar to objective structured clinical examination. The study included 159 case scenarios from providers in Canada, the United Kingdom and India, 20 primary care physicians compared to AMIE, and evaluations by specialist physicians and patient-actors. AMIE demonstrated greater diagnostic accuracy and superior performance on 30 out of 32 axes according to the specialist physicians and 25 out of 26 axes according to the patient-actors. Our research has several limitations and should be interpreted with caution. Clinicians used synchronous text chat, which permits large-scale LLM–patient interactions, but this is unfamiliar in 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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Exploring large language models for specialist-level oncology care
Vikram Dhillon
Polly Niravath
Preethi Prasad
Khaled Saab
Ryutaro Tanno
Yong Cheng
Hanh Mai
Ethan Burns
Zainub Ajmal
Kavita Kulkarni
Philip Mansfield
Joelle Barral
Juro Gottweis
Sara Mahdavi
NEJM AI (2025)
Preview abstract
Large language models have shown rapid progress in encoding clinical knowledge and demonstrating clinical reasoning. However, their capabilities in subspecialty or complex medical settings remain underexplored. In this work, we probe the performance of Articulate Medical Intelligence Explorer (AMIE), a conversational diagnostic AI system in the subspecialty of breast oncology care without specific fine-tuning to this challenging domain. To perform this evaluation, we curated a set of 60 synthetic breast cancer vignettes representing a range of treatment-naive, treatment-refractory, and rare histology cases encountered in a community-based breast oncology clinic. We developed a detailed clinical rubric for evaluating management plans, including axes such as the quality of case summarization, safety of the proposed care plan, and recommendations for treatment (i.e., chemotherapy, radiotherapy, surgery, and hormonal therapy). To improve performance, we enhanced AMIE with the inference-time ability to perform web search retrieval to gather relevant and up-to-date clinical knowledge and refine its responses with a multistage, self-critique pipeline. We compare the response quality of AMIE with that of internal medicine trainees, oncology fellows, and general oncology attendings under both automated and specialist clinician evaluations. Although our evaluations were limited to a few physicians, AMIE outperformed trainees and fellows, demonstrating the potential of the system in this important domain. However, AMIE’s performance was overall inferior to that of attending oncologists, suggesting that further prospective research is needed.
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Toward expert-level medical question answering with large language models
Karan Singhal
Juro Gottweis
Le Hou
Kevin Clark
Heather Cole-Lewis
Amy Wang
Sami Lachgar
Philip Mansfield
Sushant Prakash
Bradley Green
Ewa Dominowska
Nenad Tomašev
Renee Wong
Sara Mahdavi
Joelle Barral
Nature Medicine (2025)
Preview abstract
Large language models (LLMs) have shown promise in medical question answering, with Med-PaLM being the first to exceed a ‘passing’ score in United States Medical Licensing Examination style questions. However, challenges remain in long-form medical question answering and handling real-world workflows. Here, we present Med-PaLM 2, which bridges these gaps with a combination of base LLM improvements, medical domain fine-tuning and new strategies for improving reasoning and grounding through ensemble refinement and chain of retrieval. Med-PaLM 2 scores up to 86.5% on the MedQA dataset, improving upon Med-PaLM by over 19%, and demonstrates dramatic performance increases across MedMCQA, PubMedQA and MMLU clinical topics datasets. Our detailed human evaluations framework shows that physicians prefer Med-PaLM 2 answers to those from other physicians on eight of nine clinical axes. Med-PaLM 2 also demonstrates significant improvements over its predecessor across all evaluation metrics, particularly on new adversarial datasets designed to probe LLM limitations (P < 0.001). In a pilot study using real-world medical questions, specialists preferred Med-PaLM 2 answers to generalist physician answers 65% of the time. While specialist answers were still preferred overall, both specialists and generalists rated Med-PaLM 2 to be as safe as physician answers, demonstrating its growing potential in real-world medical applications.
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Towards accurate differential diagnosis with large language models
Daniel McDuff
Amy Wang
Karan Singhal
Yash Sharma
Kavita Kulkarni
Le Hou
Yong Cheng
Sara Mahdavi
Sushant Prakash
Anupam Pathak
Shwetak Patel
Ewa Dominowska
Juro Gottweis
Joelle Barral
Kat Chou
Jake Sunshine
Nature (2025)
Preview abstract
A comprehensive differential diagnosis is a cornerstone of medical care that is often reached through an iterative process of interpretation that combines clinical history, physical examination, investigations and procedures. Interactive interfaces powered by large language models present new opportunities to assist and automate aspects of this process. Here we introduce the Articulate Medical Intelligence Explorer (AMIE), a large language model that is optimized for diagnostic reasoning, and evaluate its ability to generate a differential diagnosis alone or as an aid to clinicians. Twenty clinicians evaluated 302 challenging, real-world medical cases sourced from published case reports. Each case report was read by two clinicians, who were randomized to one of two assistive conditions: assistance from search engines and standard medical resources; or assistance from AMIE in addition to these tools. All clinicians provided a baseline, unassisted differential diagnosis prior to using the respective assistive tools. AMIE exhibited standalone performance that exceeded that of unassisted clinicians (top-10 accuracy 59.1% versus 33.6%, P = 0.04). Comparing the two assisted study arms, the differential diagnosis quality score was higher for clinicians assisted by AMIE (top-10 accuracy 51.7%) compared with clinicians without its assistance (36.1%; McNemar’s test: 45.7, P < 0.01) and clinicians with search (44.4%; McNemar’s test: 4.75, P = 0.03). Further, clinicians assisted by AMIE arrived at more comprehensive differential lists than those without assistance from AMIE. Our study suggests that AMIE has potential to improve clinicians’ diagnostic reasoning and accuracy in challenging cases, meriting further real-world evaluation for its ability to empower physicians and widen patients’ access to specialist-level expertise.
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Preview abstract
Artificial Intelligence (AI) holds the promise of transforming healthcare by improving patient outcomes, increasing accessibility and efficiency, and decreasing the cost of care. Realizing this vision of a healthier world for everyone everywhere requires partnerships and trust between healthcare systems, clinicians, payers, technology companies, pharmaceutical companies, and governments to drive innovations in machine learning and artificial intelligence to patients. Google is one example of a technology company that is partnering with healthcare systems, clinicians, and researchers to develop technology solutions that will directly improve the lives of patients. In this chapter we share landmark trials of the use of AI in healthcare. We also describe the application of our novel system of organizing information to unify data in electronic health records (EHRs) and bring an integrated view of patient records to clinicians. We discuss our consumer focused innovation in dermatology to help guide search journeys for personalized information about skin conditions. Finally, we share a perspective on how to embed ethics and a concern for all patients into the development of 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
Sara Mahdavi
Bradley Green
Ewa Dominowska
Joelle Barral
Karan Singhal
Pete Florence
NEJM AI (2024)
Preview abstract
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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