Sunny Virmani
Sunny Virmani is a Group Product Manager at Google where he co-leads Project AMIE, Google's flagship research project for AI in clinical reasoning and conversations. Previously, Sunny spearheaded pioneering research at Google in diabetic retinopathy screening using machine learning, significantly advancing the field. He brings extensive experience in healthcare technology, having held product management leadership roles at Verily Life Sciences, Carl Zeiss Meditec, and Philips Healthcare.
Authored Publications
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Prospective evidence for conversational medical AI is hard, but non-negotiable
Adam Rodman
Ami Parekh
Ethan Goh
Christina Chen
Joelle Barral
Nature Medicine (2026)
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Trust in clinical artificial intelligence (AI) cannot be benchmarked into existence. It must be earned through rigorous prospective studies in real-world clinical settings, where the hardest lessons often concern the humans and systems around the AI, not the technology itself.
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Towards expert-level medical AI for real-time video consultations
Mahvish Nagda
Jihyeon Lee
Matthew Thompson
CJ Park
Tim Strother
Roma Ruparel
Teya Bergamaschi
Suhana Bedi
Meet Shah
Pavel Dubov
Toshiyuki Fukuzawa
Sam Schmidgall
Craig Schiff
Joseph Xu
Aliya Rysbek
Yana Lunts
Jan Freyberg
Rebecca Hemenway
David Racz
Carey Radebaugh
Joelle Barral
Kavi Goel
Kat Chou
James Manyika
Gregory Wayne
Yun Liu
Ethan Goh
Christina Chen
Ryutaro Tanno
arXiv (2026)
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Audio-visual interaction is the standard for patient-physician consultations, enabling natural communication and effective assessment of illness through non-verbal cues. While text-based AI has shown promise, it discards essential perceptual dimensions and limits patients who cannot articulate symptoms in writing. Early efforts to extend medical AI to audio-visual interaction have demonstrated feasibility, but not reached clinician-level performance. Here, we provide the first demonstration of expert-level AI in real-time clinical video consultations using AMIE (Articulate Medical Intelligence Explorer) in a video configuration. AMIE (Video) is a Gemini-based multi-agent system integrating low-latency dialogue, clinical reasoning, and real-time audio-visual perception. To guide development, we established a taxonomy and automated evaluations for clinical audio-visual cues in telehealth settings. In a randomized Objective Structured Clinical Examination (OSCE) study with 30 primary care physicians (PCPs), 15 patient actors and 100 clinical scenarios, we compared AMIE (Video), its text-only counterpart AMIE (Text), and PCPs consulting via video. Clinical evaluators rated AMIE (Video) on par or better than PCPs in history-taking, diagnosis, management, and physical observation and examination. Patient actors preferred AMIE's approach to assessing and explaining conditions, while PCPs were preferred for rapport and partnership building. In modality ablation, patient actors preferred AMIE (Video)'s interface over text chat for communicative effectiveness, convenience, and feeling understood. Limitations remain in fine anatomical precision, subtle affective nuances, and high-frequency movements. While further research is needed before real-world translation, these results mark an important milestone toward AI systems capable of augmenting care across the sensory complexity of clinical practice.
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Validation of a Deep Learning Model for Diabetic Retinopathy on Patients with Young-Onset Diabetes
Tony Tan-Torres
Pradeep Praveen
Divleen Jeji
Arthur Brant
Xiang Yin
Lu Yang
Tayyeba Ali
Ilana Traynis
Dushyantsinh Jadeja
Rajroshan Sawhney
Pradeep Venkatesh
Nikhil Tandon
Ophthalmology and Therapy (2025)
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Introduction
While many deep learning systems (DLSs) for diabetic retinopathy (DR) have been developed and validated on cohorts with an average age of 50s or older, fewer studies have examined younger individuals. This study aimed to understand DLS performance for younger individuals, who tend to display anatomic differences, such as prominent retinal sheen. This sheen can be mistaken for exudates or cotton wool spots, and potentially confound DLSs.
Methods
This was a prospective cross-sectional cohort study in a “Diabetes of young” clinic in India, enrolling 321 individuals between ages 18 and 45 (98.8% with type 1 diabetes). Participants had fundus photographs taken and the photos were adjudicated by experienced graders to obtain reference DR grades. We defined a younger cohort (age 18–25) and an older cohort (age 26–45) and examined differences in DLS performance between the two cohorts. The main outcome measures were sensitivity and specificity for DR.
Results
Eye-level sensitivity for moderate-or-worse DR was 97.6% [95% confidence interval (CI) 91.2, 98.2] for the younger cohort and 94.0% [88.8, 98.1] for the older cohort (p = 0.418 for difference). The specificity for moderate-or-worse DR significantly differed between the younger and older cohorts, 97.9% [95.9, 99.3] and 92.1% [87.6, 96.0], respectively (p = 0.008). Similar trends were observed for diabetic macular edema (DME); sensitivity was 79.0% [57.9, 93.6] for the younger cohort and 77.5% [60.8, 90.6] for the older cohort (p = 0.893), whereas specificity was 97.0% [94.5, 99.0] and 92.0% [88.2, 95.5] (p = 0.018). Retinal sheen presence (94% of images) was associated with DME presence (p < 0.0001). Image review suggested that sheen presence confounded reference DME status, increasing noise in the labels and depressing measured sensitivity. The gradability rate for both DR and DME was near-perfect (99% for both).
Conclusion
DLS-based DR screening performed well in younger individuals aged 18–25, with comparable sensitivity and higher specificity compared to individuals aged 26–45. Sheen presence in this cohort made identification of DME difficult for graders and depressed measured DLS sensitivity; additional studies incorporating optical coherence tomography may improve accuracy of measuring DLS DME sensitivity.
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Performance of a Deep Learning Diabetic Retinopathy Algorithm in India
Arthur Brant
Xiang Yin
Lu Yang
Divleen Jeji
Anchintha Meenu
Naresh Babu Kannan
Florence Thng
Lily Peng
Ramasamy Kim
JAMA Network Open (2025)
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Importance: While prospective studies have investigated the accuracy of artificial intelligence (AI) for detection of diabetic retinopathy (DR) and diabetic macular edema (DME), to date, little published data exist on the clinical performance of these algorithms.
Objective: To evaluate the clinical performance of an automated retinal disease assessment (ARDA) algorithm in the postdeployment setting at Aravind Eye Hospital in India.
Design, Setting, and Participants: This cross-sectional analysis involved an approximate 1% sample of fundus photographs from patients screened using ARDA. Images were graded via adjudication by US ophthalmologists for DR and DME, and ARDA’s output was compared against the adjudicated grades at 45 sites in Southern India. Patients were randomly selected between January 1, 2019, and July 31, 2023.
Main Outcomes and Measures: Primary analyses were the sensitivity and specificity of ARDA for severe nonproliferative DR (NPDR) or proliferative DR (PDR). Secondary analyses focused on sensitivity and specificity for sight-threatening DR (STDR) (DME or severe NPDR or PDR).
Results: Among the 4537 patients with 4537 images with adjudicated grades, mean (SD) age was 55.2 (11.9) years and 2272 (50.1%) were male. Among the 3941 patients with gradable photographs, 683 (17.3%) had any DR, 146 (3.7%) had severe NPDR or PDR, 109 (2.8%) had PDR, and 398 (10.1%) had STDR. ARDA’s sensitivity and specificity for severe NPDR or PDR were 97.0% (95% CI, 92.6%-99.2%) and 96.4% (95% CI, 95.7%-97.0%), respectively. Positive predictive value (PPV) was 50.7% and negative predictive value (NPV) was 99.9%. The clinically important miss rate for severe NPDR or PDR was 0% (eg, some patients with severe NPDR or PDR were interpreted as having moderate DR and referred to clinic). ARDA’s sensitivity for STDR was 95.9% (95% CI, 93.0%-97.4%) and specificity was 94.9% (95% CI, 94.1%-95.7%); PPV and NPV were 67.9% and 99.5%, respectively.
Conclusions and Relevance: In this cross-sectional study investigating the clinical performance of ARDA, sensitivity and specificity for severe NPDR and PDR exceeded 96% and caught 100% of patients with severe NPDR and PDR for ophthalmology referral. This preliminary large-scale postmarketing report of the performance of ARDA after screening 600 000 patients in India underscores the importance of monitoring and publication an algorithm's clinical performance, consistent with recommendations by regulatory bodies.
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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)
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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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Creating an Empirical Dermatology Dataset Through Crowdsourcing With Web Search Advertisements
Abbi Ward
Jimmy Li
Julie Wang
Sriram Lakshminarasimhan
Ashley Carrick
Jay Hartford
Pradeep Kumar S
Renee Wong
Margaret Ann Smith
Dawn Siegel
Steven Lin
Justin Ko
JAMA Network Open (2024)
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Importance: Health datasets from clinical sources do not reflect the breadth and diversity of disease, impacting research, medical education, and artificial intelligence tool development. Assessments of novel crowdsourcing methods to create health datasets are needed.
Objective: To evaluate if web search advertisements (ads) are effective at creating a diverse and representative dermatology image dataset.
Design, Setting, and Participants: This prospective observational survey study, conducted from March to November 2023, used Google Search ads to invite internet users in the US to contribute images of dermatology conditions with demographic and symptom information to the Skin Condition Image Network (SCIN) open access dataset. Ads were displayed against dermatology-related search queries on mobile devices, inviting contributions from adults after a digital informed consent process. Contributions were filtered for image safety and measures were taken to protect privacy. Data analysis occurred January to February 2024.
Exposure: Dermatologist condition labels as well as estimated Fitzpatrick Skin Type (eFST) and estimated Monk Skin Tone (eMST) labels.
Main Outcomes and Measures: The primary metrics of interest were the number, quality, demographic diversity, and distribution of clinical conditions in the crowdsourced contributions. Spearman rank order correlation was used for all correlation analyses, and the χ2 test was used to analyze differences between SCIN contributor demographics and the US census.
Results: In total, 5749 submissions were received, with a median of 22 (14-30) per day. Of these, 5631 (97.9%) were genuine images of dermatological conditions. Among contributors with self-reported demographic information, female contributors (1732 of 2596 contributors [66.7%]) and younger contributors (1329 of 2556 contributors [52.0%] aged <40 years) had a higher representation in the dataset compared with the US population. Of 2614 contributors who reported race and ethnicity, 852 (32.6%) reported a racial or ethnic identity other than White. Dermatologist confidence in assigning a differential diagnosis increased with the number of self-reported demographic and skin-condition–related variables (Spearman R = 0.1537; P < .001). Of 4019 contributions reporting duration since onset, 2170 (54.0%) reported onset within less than 7 days of submission. Of the 2835 contributions that could be assigned a dermatological differential diagnosis, 2523 (89.0%) were allergic, infectious, or inflammatory conditions. eFST and eMST distributions reflected the geographical origin of the dataset.
Conclusions and Relevance: The findings of this survey study suggest that search ads are effective at crowdsourcing dermatology images and could therefore be a useful method to create health datasets. The SCIN dataset bridges important gaps in the availability of images of common, short-duration skin conditions.
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Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study
Terry Spitz
Malcolm Chelliah
Heather Cole-Lewis
Donald Martin
Tiam Jaroensri
Geoff Keeling
Stephanie Farquhar
Qinghan Xue
Jenna Lester
Cían Hughes
Patricia Strachan
Fraser Tan
Peggy Bui
Craig Mermel
Lily Peng
Ivor Horn
The Lancet eClinicalMedicine (2024)
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Background
Artificial intelligence (AI) has repeatedly been shown to encode historical inequities in healthcare. We aimed to develop a framework to quantitatively assess the performance equity of health AI technologies and to illustrate its utility via a case study.
Methods
Here, we propose a methodology to assess whether health AI technologies prioritise performance for patient populations experiencing worse outcomes, that is complementary to existing fairness metrics. We developed the Health Equity Assessment of machine Learning performance (HEAL) framework designed to quantitatively assess the performance equity of health AI technologies via a four-step interdisciplinary process to understand and quantify domain-specific criteria, and the resulting HEAL metric. As an illustrative case study (analysis conducted between October 2022 and January 2023), we applied the HEAL framework to a dermatology AI model. A set of 5420 teledermatology cases (store-and-forward cases from patients of 20 years or older, submitted from primary care providers in the USA and skin cancer clinics in Australia), enriched for diversity in age, sex and race/ethnicity, was used to retrospectively evaluate the AI model's HEAL metric, defined as the likelihood that the AI model performs better for subpopulations with worse average health outcomes as compared to others. The likelihood that AI performance was anticorrelated to pre-existing health outcomes was estimated using bootstrap methods as the probability that the negated Spearman's rank correlation coefficient (i.e., “R”) was greater than zero. Positive values of R suggest that subpopulations with poorer health outcomes have better AI model performance. Thus, the HEAL metric, defined as p (R >0), measures how likely the AI technology is to prioritise performance for subpopulations with worse average health outcomes as compared to others (presented as a percentage below). Health outcomes were quantified as disability-adjusted life years (DALYs) when grouping by sex and age, and years of life lost (YLLs) when grouping by race/ethnicity. AI performance was measured as top-3 agreement with the reference diagnosis from a panel of 3 dermatologists per case.
Findings
Across all dermatologic conditions, the HEAL metric was 80.5% for prioritizing AI performance of racial/ethnic subpopulations based on YLLs, and 92.1% and 0.0% respectively for prioritizing AI performance of sex and age subpopulations based on DALYs. Certain dermatologic conditions were significantly associated with greater AI model performance compared to a reference category of less common conditions. For skin cancer conditions, the HEAL metric was 73.8% for prioritizing AI performance of age subpopulations based on DALYs.
Interpretation
Analysis using the proposed HEAL framework showed that the dermatology AI model prioritised performance for race/ethnicity, sex (all conditions) and age (cancer conditions) subpopulations with respect to pre-existing health disparities. More work is needed to investigate ways of promoting equitable AI performance across age for non-cancer conditions and to better understand how AI models can contribute towards improving equity in health outcomes.
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Lessons learned from translating AI from development to deployment in healthcare
Elin Rønby Pedersen
Divleen Jeji
Lily Peng
Nature Medicine (2023)
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The application of an artificial intelligence (AI)-based screening tool for retinal disease in India and Thailand highlighted the myths and reality of introducing medical AI, which may form a framework for subsequent tools.
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Risk Stratification for Diabetic Retinopathy Screening Order Using Deep Learning: A Multicenter Prospective Study
Ashish Bora
Rayman Huang
Ilana Traynis
Lily Peng
Avinash Varadarajan
Warisara Pattanapongpaiboon
Reena Chopra
Dr. Paisan Raumviboonsuk
Translational Vision Science & Technology (2023)
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Purpose: Real-world evaluation of a deep learning model that prioritizes patients based on risk of progression to moderate or worse (MOD+) diabetic retinopathy (DR).
Methods: This nonrandomized, single-arm, prospective, interventional study included patients attending DR screening at four centers across Thailand from September 2019 to January 2020, with mild or no DR. Fundus photographs were input into the model, and patients were scheduled for their subsequent screening from September 2020 to January 2021 in order of predicted risk. Evaluation focused on model sensitivity, defined as correctly ranking patients that developed MOD+ within the first 50% of subsequent screens.
Results: We analyzed 1,757 patients, of which 52 (3.0%) developed MOD+. Using the model-proposed order, the model's sensitivity was 90.4%. Both the model-proposed order and mild/no DR plus HbA1c had significantly higher sensitivity than the random order (P < 0.001). Excluding one major (rural) site that had practical implementation challenges, the remaining sites included 567 patients and 15 (2.6%) developed MOD+. Here, the model-proposed order achieved 86.7% versus 73.3% for the ranking that used DR grade and hemoglobin A1c.
Conclusions: The model can help prioritize follow-up visits for the largest subgroups of DR patients (those with no or mild DR). Further research is needed to evaluate the impact on clinical management and outcomes.
Translational Relevance: Deep learning demonstrated potential for risk stratification in DR screening. However, real-world practicalities must be resolved to fully realize the benefit.
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Performance of a Diabetic Retinopathy Artificial Intelligence Algorithm for Ultra-widefield Imaging
Tunde Peto
Lloyd Aiello
Srinivas R Sadda
Drew Lewis
Anne Marie Cairns
Dana Keane
Jerry Cavallerano
Barba Hamill
Lily Peng
Sara Ellen Godek
Lu Yang
Naho Kitade
Kira Whitehouse
ARVO (2022)
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Purpose: To evaluate the performance of a deep learning model for diabetic retinopathy (DR) and diabetic macular edema screening when using ultra-widefield (UWF) imaging.
Methods: For model development, 67,200 UWF images were collected from DR programs and ophthalmology clinics worldwide. 30,836 images were double graded and adjudicated at 8 grading centres by 125 certified graders using ETDRS extension of the Modified Airlie House Classification of Diabetic Retinopathy following the JVN Clinical Trial Ultrawide Field Grading Manual v1.0. The grading system used traditional ETDRS 7-SF field definition as well as extended fields 3-7 to evaluate the retinal periphery. A further 36,364 UWF images were graded using a grading protocol based on the ICDR classification. The dataset was split into training, tuning and testing. The final DR model is an ensemble of 10 EfficientNet-b0 neural networks, independently trained with standard image augmentation techniques. For model validation, two independent sets of images were collected. Model performance was evaluated by comparing its predictions to the adjudicated ground truth for both sets of images.
Results: Prior to clinical validation, the model performance was internally evaluated on an independent set of 1967 images, of which 1050 were graded via adjudication as negative for more than mild diabetic retinopathy (mtmDR negative), and 917 as having referable diabetic retinopathy (mtmDR positive). The overall performance (Table 1) was weighted by target DR distribution. Clinical validation evaluated an independent data set of 420 images selected to achieve a target distribution that enabled appropriate confidence intervals for mtmDR sensitivity and specificity A panel of three graders adjudicated these 420 images and assessed 241 as mtmDR negative, 179 as mtmDR positive and 135 as vtDR positive. Model’s performance on the clinical validation set is shown in Table 2.
Conclusions: The deep learning model was developed with high quality graded UWF images and performed at a level that highly suggests usefulness in a clinical screening setting. A large, prospective multi-center clinical trial is currently evaluating the performance of a similar model in a real-world clinical setting.
This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.
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