Yun Liu
Yun is a senior staff research scientist in Google Research. In this role he focuses on developing and validating machine learning for medical applications across multiple fields: pathology, ophthalmology, radiology, dermatology, and more. Yun completed his PhD at Harvard-MIT Health Sciences and Technology, where he worked on predictive risk modeling using biomedical signals, medical text, and billing codes. He has previously also worked on predictive modeling for nucleic acid sequences and protein structures. Yun completed a B.S. in Molecular and Cellular Biology and Computer Science at Johns Hopkins University.
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SymptomAI: Toward a Conversational AI Agent for Everyday Symptom Assessment
Joe Breda
Fadi Yousif
Beszel Hawkins
Marinela Cotoi
Miao Liu
Ray Luo
Sam Schmidgall
Girish Narayanswamy
Samuel Solomon
Max Xu
Longfei Shangguan
Bhavna Daryani
Buddy Herkenham
Cara Tan
Mark Malhotra
Shwetak Patel
Zach Wasson
Dimitrios Antos
Bob Lou
Matthew Thompson
Jonathan Richina
Anupam Pathak
Nichole Young-Lin
Jake Sunshine
Daniel McDuff
Arxiv preprint, 2605.040 (2026) (to appear)
Preview abstract
Language models excel at diagnostic assessments on curated medical case-studies and vignettes, performing on par with, or better than, clinical professionals. However, existing studies focus on complex scenarios with rich context making it difficult to draw conclusions about how these systems perform for patients reporting symptoms in everyday life. We deployed SymptomAI, a set of conversational AI agents for end-to-end patient interviewing and differential diagnosis (DDx), via the Fitbit app in a study that randomized participants (N=13,917) to interact with five AI agents. This corpus captures diverse communication and a realistic distribution of illnesses from a real world population. A subset of 1,228 participants reported a clinician-provided diagnosis, and 517 of these were further evaluated by a panel of clinicians during over 250 hours of annotation. SymptomAI DDx were significantly more accurate (OR = 2.56, p < 0.001) than those from independent clinicians given the same dialogue in a blinded randomized comparison. Moreover, agentic strategies which conduct a dedicated symptom interview that elicit additional symptom information before providing a diagnosis, perform substantially better than baseline, user-guided conversations (p < 0.001). An auxiliary analysis on 1,509 conversations from a general US population panel validated that these results generalize beyond wearable device users. We used SymptomAI diagnoses as labels for all 13,917 participants to analyze over 500,000 days of wearable metrics across nearly 400 unique conditions. We identified strong associations between acute infections and physiological shifts (e.g., OR > 7 for influenza). While limited by self-reported ground truth, these results demonstrate the benefits of a dedicated and complete symptom interview compared to a user-guided symptom discussion, which is the default of most consumer LLMs.
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Toward a test of medical AI superintelligence
Ethan Goh
David Wu
Chase Walton
Liam McCoy
Anastasia Perez
Laura Wegner
Fateme Nateghi Haredasht
Luyang Luo
Kathleen Lacar
Thomas Buckley
Austin Schoeffler
Peter Brodeur
Kameron C. Black
John Havlik
John Rumsfeld
Daniel Lopez-martinez
Paxton Maeder-York
Karan Singhal
David Gunning
Bon Ku
Haider Warraich
Shantanu Nundy
Vishnu Ravi
Arnold Milstein
Jason Hom
Kevin Schulman
Pranav Rajpurkar
Arjun Manrai
Robert Wachter, MD
Eric Topol
Eric horvitz
Adam Rodman
Jonathan Chen
Nature Medicine (2026)
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Researchers urgently need a rigorous, task-based framework to define and measure medical AI ‘superintelligence’, because existing benchmarks are misleading and insufficient.
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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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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)
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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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Oculomics: Current Concepts and Evidence
Zhuoting Zhu
Yueye Wang
Ziyi Qi
Wenyi Hu
Xiayin Zhang
Siegfried Wagner
Yujie Wang
An Ran Ran
Joshua Ong
Ethan Waisberg
Mouayad Masalkhi
Alex Suh
Yih Chung Tham
Carol Y. Cheung
Xiaohong Yang
Honghua Yu
Zongyuan Ge
Wei Wang
Bin Sheng
Andrew G. Lee
Alastair Denniston
Peter van Wijngaarden
Pearse Keane
Ching-Yu Cheng
Mingguang He
Tien Yin Wong
Progress in Retinal and Eye Research (2025)
Preview abstract
The eye provides novel insights into general health, as well as pathogenesis and development of systemic diseases. In the past decade, growing evidence has demonstrated that the eye's structure and function mirror multiple systemic health conditions, especially in cardiovascular diseases, neurodegenerative disorders, and kidney impairments. This has given rise to the field of oculomics- the application of ophthalmic biomarkers to understand mechanisms, detect and predict disease. The development of this field has been accelerated by three major advances: 1) the availability and widespread clinical adoption of high-resolution and non-invasive ophthalmic imaging (“hardware”); 2) the availability of large studies to interrogate associations (“big data”); 3) the development of novel analytical methods, including artificial intelligence (AI) (“software”). Oculomics offers an opportunity to enhance our understanding of the interplay between the eye and the body, while supporting development of innovative diagnostic, prognostic, and therapeutic tools. These advances have been further accelerated by developments in AI, coupled with large-scale linkage datasets linking ocular imaging data with systemic health data. Oculomics also enables the detection, screening, diagnosis, and monitoring of many systemic health conditions. Furthermore, oculomics with AI allows prediction of the risk of systemic diseases, enabling risk stratification, opening up new avenues for prevention or individualized risk prediction and prevention, facilitating personalized medicine. In this review, we summarise current concepts and evidence in the field of oculomics, highlighting the progress that has been made, remaining challenges, and the opportunities for future research.
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PolyPath: Adapting a Large Multimodal Model for Multislide Pathology Report Generation
Lin Yang
Shravya Shetty
Tiam Jaroensri
Faruk Ahmed
Daniel Golden
Modern Pathology (2025)
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The interpretation of histopathology cases underlies many important diagnostic and treatment decisions in medicine. Notably, this process typically requires pathologists to integrate and summarize findings across multiple slides per case. Existing vision-language capabilities in computational pathology have so far been largely limited to small regions of interest, larger regions at low magnification, or single whole-slide images (WSIs). This limits interpretation of findings that span multiple high-magnification regions across multiple WSIs. By making use of Gemini 1.5 Flash, a large multimodal model with a 1-million token context window, we demonstrate the ability to generate bottom-line diagnoses from up to 40,000 image patches of size 768 × 768 pixels from multiple WSIs at 10× magnification. This is the equivalent of up to 11 hours of video at 1 fps. Expert pathologist evaluations demonstrate that the generated report text is clinically accurate and equivalent to or preferred over the original reporting for 68% (95% CI, 60%-76%) of multi-slide examples with up to 5 slides. Although performance decreased for examples with ≥6 slides, this study demonstrates the promise of leveraging the long-context capabilities of modern large multimodal models for the uniquely challenging task of medical report generation where each case can contain thousands of image patches.
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RADAR: Benchmarking Language Models on Imperfect Tabular Data
Ken Gu
Kumar Ayush
Hong Yu
Zhihan Zhang
Yuzhe Yang
Shwetak Patel
Max Xu
Mark Malhotra
Orson Xu
Evelyn Zhang
Tim Althoff
2025
Preview abstract
Language models (LMs) are increasingly being deployed to perform autonomous data analyses, yet their~\textit{\robustnessTerm}-- the ability to recognize, reason over, and appropriately handle data artifacts such as missing values, outliers, and logical inconsistencies—remains under-explored. These artifacts are common in real-world tabular data and, if mishandled, can significantly compromise the validity of analytical conclusions. To address this gap, we present RADAR, a benchmark for systematically evaluating data awareness on tabular data. RADAR introduces programmatic perturbations for each unique query table pair, enabling targeted evaluation of model behavior. RADAR~ comprises 2500 queries for data analysis across 55 datasets spanning 20 domains and 5 data awareness dimensions. In addition to evaluating artifact handling, RADAR systematically varies table size to study how reasoning performance scales with input length. In our evaluation, we identify fundamental gaps in their ability to perform reliable, data-aware analyses. Designed to be flexible and extensible, RADAR supports diverse perturbation types and controllable table sizes, offering a valuable resource for advancing tabular reasoning.
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Unprecedented Insights into Maternal Sleep: A Large-scale Longitudinal Analysis of Real-world Wearable Device Data Before, During, and After Pregnancy
Nichole Young-Lin
Conor Heneghan
Logan Niehaus
Ariel Haney
Karla Gleichauf
Jacqueline Shreibati
Belen Lafon
Lancet eBioMedicine (2025)
Preview abstract
Introduction: Current understanding of pregnancy and postpartum sleep is driven by limited lab or self-reported data. Consumer wearable devices may help reveal longitudinal, real-world sleep patterns.
Methods: We analyzed de-identified wearable device data from 2,540 users in the United States and Canada who met strict wear-time requirements (≥80% daily usage for ≥80% of the time periods of interest [12 weeks prepregnancy, throughout pregnancy, and 20 weeks immediately postpartum]). We tracked sleep time and staging using Fitbit devices.
Results: Compared to prepregnancy, total sleep time (TST) increased from an average of 425.3±43.5 min to a peak of 447.6±47.6 min at gestational week 10 with ongoing declines throughout pregnancy. Time in bed (TIB) followed a similar pattern. Increased light sleep drove the initial TST rise. Deep and REM sleep decreased significantly throughout pregnancy, with maximum reductions of 19.2±13.8 min (p<0.01) and 9.0±19.2 min (p<0.01) respectively by pregnancy end. Sleep efficiency also declined slightly during pregnancy (median drop from 88.3% to 86.8%). After delivery, TIB remained below the prepregnancy baseline by 14.7±45.7 min at one year postpartum and 15.2±47.7 min at 1.5 years postpartum.
Conclusion: This unprecedented look at large-scale, real-world sleep and pregnancy patterns revealed a previously unquantified initial increase in sleep followed by decreases in both quantity and quality as pregnancy progresses. Sleep deficits persist for at least 1.5 years postpartum. These quantified trends can assist clinicians and patients in understanding what to expect.
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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)
Preview abstract
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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Scaling Wearable Foundation Models
Girish Narayanswamy
Kumar Ayush
Yuzhe Yang
Orson Xu
Shun Liao
Shyam Tailor
Jake Sunshine
Tim Althoff
Shrikanth (Shri) Narayanan
Jiening Zhan
Mark Malhotra
Shwetak Patel
Samy Abdel-Ghaffar
Daniel McDuff
2025
Preview abstract
Wearable sensors have become ubiquitous thanks to a variety of health tracking features. The resulting continuous and longitudinal measurements from everyday life generate large volumes of data. However, making sense of these observations for scientific and actionable insights is non-trivial. Inspired by the empirical success of generative modeling, where large neural networks learn powerful representations from vast amounts of text, image, video, or audio data, we investigate the scaling properties of wearable sensor foundation models across compute, data, and model size. Using a dataset of up to 40 million hours of in-situ heart rate, heart rate variability, accelerometer, electrodermal activity, skin temperature, and altimeter per-minute data from over 165,000 people, we create LSM, a multimodal foundation model built on the largest wearable-signals dataset with the most extensive range of sensor modalities to date. Our results establish the scaling laws of LSM for tasks such as imputation, interpolation and extrapolation across both time and sensor modalities. Moreover, we highlight how LSM enables sample-efficient downstream learning for tasks including exercise and activity recognition.
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