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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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
Sunny Virmani
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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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 Schneider
Logan Niehaus
Ariel Haney
Karla Gleichauf
Jacqueline Shreibati
Belen Lafon
Lancet eBioMedicine (2025)
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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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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)
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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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Closing the AI generalisation gap by adjusting for dermatology condition distribution differences across clinical settings
Rajeev Rikhye
Aaron Loh
Grace Hong
Margaret Ann Smith
Vijaytha Muralidharan
Doris Wong
Michelle Phung
Nicolas Betancourt
Bradley Fong
Rachna Sahasrabudhe
Khoban Nasim
Alec Eschholz
Basil Mustafa
Jan Freyberg
Terry Spitz
Kat Chou
Peggy Bui
Justin Ko
Steven Lin
The Lancet eBioMedicine (2025)
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Background: Generalisation of artificial intelligence (AI) models to a new setting is challenging. In this study, we seek to understand the robustness of a dermatology (AI) model and whether it generalises from telemedicine cases to a new setting including both patient-submitted photographs (“PAT”) and clinician-taken photographs in-clinic (“CLIN”).
Methods: A retrospective cohort study involving 2500 cases previously unseen by the AI model, including both PAT and CLIN cases, from 22 clinics in the San Francisco Bay Area, spanning November 2015 to January 2021. The primary outcome measure for the AI model and dermatologists was the top-3 accuracy, defined as whether their top 3 differential diagnoses contained the top reference diagnosis from a panel of dermatologists per case.
Findings: The AI performed similarly between PAT and CLIN images (74% top-3 accuracy in CLIN vs. 71% in PAT), however, dermatologists were more accurate in PAT images (79% in CLIN vs. 87% in PAT). We demonstrate that demographic factors were not associated with AI or dermatologist errors; instead several categories of conditions were associated with AI model errors (p < 0.05). Resampling CLIN and PAT to match skin condition distributions to the AI development dataset reduced the observed differences (AI: 84% CLIN vs. 79% PAT; dermatologists: 77% CLIN vs. 89% PAT). We demonstrate a series of steps to close the generalisation gap, requiring progressively more information about the new dataset, ranging from the condition distribution to additional training data for rarer conditions. When using additional training data and testing on the dataset without resampling to match AI development, we observed comparable performance from end-to-end AI model fine tuning (85% in CLIN vs. 83% in PAT) vs. fine tuning solely the classification layer on top of a frozen embedding model (86% in CLIN vs. 84% in PAT).
Interpretation: AI algorithms can be efficiently adapted to new settings without additional training data by recalibrating the existing model, or with targeted data acquisition for rarer conditions and retraining just the final layer.
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Passive Heart Rate Monitoring During Smartphone Use in Everyday Life
Shun Liao
Paolo Di Achille
Jiang Wu
Silviu Borac
Jonathan Wang
Eric Teasley
Lawrence Cai
Daniel McDuff
Hao-Wei Su
Brent Winslow
Anupam Pathak
Shwetak Patel
Jim Taylor
Jamie Rogers
(2025)
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Resting heart rate (RHR) is an important biomarker of cardiovascular health and mortality, but tracking it longitudinally generally requires a wearable device, limiting its availability. We present PHRM, a deep learning system for passive heart rate (HR) and RHR measurements during ordinary smartphone use, using facial video-based photoplethysmography. Our system was developed using 225,773 videos from 495 participants and validated on 185,970 videos from 205 participants in laboratory and free-living conditions – the largest validation study of its kind. Compared to reference electrocardiogram, PHRM achieved a mean absolute percentage error (MAPE) <10% for HR measurements across three skin tone groups of light, medium and dark pigmentation; MAPE for each skin tone group was non-inferior versus the others. Daily RHR measured by PHRM had a mean absolute error <5 bpm compared to a wearable HR tracker, and was associated with known risk factors. These results highlight the potential of smartphones to enable passive and equitable heart health monitoring.
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LLM-based Lossless Text Simplification and its Effect on User Comprehension and Cognitive Load
Theo Guidroz
Diego Ardila
Jimmy Li
Adam Mansour
Paul Jhun
Nina Gonzalez
Xiang Ji
Mike Sanchez
Miguel Ángel Garrido
Divyansh Choudhary
Jay Hartford
Georgina Xu
Henry Serrano
Yifan Wang
Jeff Shaffer
Eric (Yifan) Cao
Sho Fujiwara
Peggy Bui
arXiv (2025)
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Information on the web, such as scientific publications and Wikipedia, often surpasses users' reading level. To help address this, we used a self-refinement approach to develop a LLM capability for minimally lossy text simplification. To validate our approach, we conducted a randomized study involving 4563 participants and 31 texts spanning 6 broad subject areas: PubMed (biomedical scientific articles), biology, law, finance, literature/philosophy, and aerospace/computer science. Participants were randomized to viewing original or simplified texts in a subject area, and answered multiple-choice questions (MCQs) that tested their comprehension of the text. The participants were also asked to provide qualitative feedback such as task difficulty. Our results indicate that participants who read the simplified text answered more MCQs correctly than their counterparts who read the original text (3.9% absolute increase, p<0.05). This gain was most striking with PubMed (14.6%), while more moderate gains were observed for finance (5.5%), aerospace/computer science (3.8%) domains, and legal (3.5%). Notably, the results were robust to whether participants could refer back to the text while answering MCQs. The absolute accuracy decreased by up to ~9% for both original and simplified setups where participants could not refer back to the text, but the ~4% overall improvement persisted. Finally, participants' self-reported perceived ease based on a simplified NASA Task Load Index was greater for those who read the simplified text (absolute change on a 5-point scale 0.33, p<0.05). This randomized study, involving an order of magnitude more participants than prior works, demonstrates the potential of LLMs to make complex information easier to understand. Our work aims to enable a broader audience to better learn and make use of expert knowledge available on the web, improving information accessibility.
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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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Performance of a Deep Learning Diabetic Retinopathy Algorithm in India
Arthur Brant
Xiang Yin
Lu Yang
Jay Nayar
Divleen Jeji
Sunny Virmani
Anchintha Meenu
Naresh Babu Kannan
Florence Thng
Lily Peng
Ramasamy Kim
JAMA Network Open (2025)
Preview abstract
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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Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the United States and Japan
Atilla Kiraly
Corbin Cunningham
Ryan Najafi
Jie Yang
Chuck Lau
Diego Ardila
Scott Mayer McKinney
Rory Pilgrim
Mozziyar Etemadi
Sunny Jansen
Lily Peng
Shravya Shetty
Neeral Beladia
Krish Eswaran
Radiology: Artificial Intelligence (2024)
Preview abstract
Lung cancer is the leading cause of cancer death world-wide with 1.8 million deaths in 20201. Studies have concluded that low-dose computed tomography lung cancer screening can reduce mortality by up to 61%2 and updated 2021 US guidelines expanded eligibility. As screening efforts rise, AI can play an important role, but must be unobtrusively integrated into existing clinical workflows. In this work, we introduce a state-of-the-art, cloud-based AI system providing lung cancer risk assessments without requiring any user input. We demonstrate its efficacy in assisting lung cancer screening under both US and Japanese screening settings using different patient populations and screening protocols. Technical improvements over a previously described system include a focus on earlier cancer detection for improved accuracy, introduction of an effective assistive user interface, and a system designed to integrate into typical clinical workflows. The stand-alone AI system was evaluated on 3085 individuals achieving area under the curve (AUC) scores of 91.7% (95%CI [89.6, 95.2]), 93.3% (95%CI [90.2, 95.7]), and 89.1% (95%CI [77.7, 97.3]) on three datasets (two from US and one from Japan), respectively. To evaluate the system’s assistive ability, we conducted two retrospective multi-reader multi-case studies on 627 cases read by experienced board certified radiologists (average 20 years of experience [7,40]) using local PACS systems in the respective US and Japanese screening settings. The studies measured the reader’s level of suspicion (LoS) and categorical responses for scores and management recommendations under country-specific screening protocols. The radiologists’ AUC for LoS increased with AI assistance by 2.3% (95%CI [0.1-4.5], p=0.022) for the US study and by 2.3% (95%CI [-3.5-8.1], p=0.179) for the Japan study. Specificity for recalls increased by 5.5% (95%CI [2.7-8.5], p<0.0001) for the US and 6.7% (95%CI [4.7-8.7], p<0.0001) for the Japan study. No significant reduction in other metrics occured. This work advances the state-of-the-art in lung cancer detection, introduces generalizable interface concepts that can be applicable to similar AI applications, and demonstrates its potential impact on diagnostic AI in global lung cancer screening with results suggesting a substantial drop in unnecessary follow-up procedures without impacting sensitivity.
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Prospective Multi-Site Validation of AI to Detect Tuberculosis and Chest X-Ray Abnormalities
Sahar Kazemzadeh
Atilla Kiraly
Nsala Sanjase
Minyoi Maimbolwa
Brian Shuma
Shahar Jamshy
Christina Chen
Arnav Agharwal
Chuck Lau
Daniel Golden
Jin Yu
Eric Wu
Kat Chou
Shravya Shetty
Krish Eswaran
Rory Pilgrim
Monde Muyoyeta
NEJM AI (2024)
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Background
Using artificial intelligence (AI) to interpret chest X-rays (CXRs) could support accessible triage tests for active pulmonary tuberculosis (TB) in resource-constrained settings.
Methods
The performance of two cloud-based CXR AI systems — one to detect TB and the other to detect CXR abnormalities — in a population with a high TB and human immunodeficiency virus (HIV) burden was evaluated. We recruited 1978 adults who had TB symptoms, were close contacts of known TB patients, or were newly diagnosed with HIV at three clinical sites. The TB-detecting AI (TB AI) scores were converted to binary using two thresholds: a high-sensitivity threshold and an exploratory threshold designed to resemble radiologist performance. Ten radiologists reviewed images for signs of TB, blinded to the reference standard. Primary analysis measured AI detection noninferiority to radiologist performance. Secondary analysis evaluated AI detection as compared with the World Health Organization (WHO) targets (90% sensitivity, 70% specificity). Both used an absolute margin of 5%. The abnormality-detecting AI (abnormality AI) was evaluated for noninferiority to a high-sensitivity target suitable for triaging (90% sensitivity, 50% specificity).
Results
Of the 1910 patients analyzed, 1827 (96%) had conclusive TB status, of which 649 (36%) were HIV positive and 192 (11%) were TB positive. The TB AI’s sensitivity and specificity were 87% and 70%, respectively, at the high-sensitivity threshold and 78% and 82%, respectively, at the balanced threshold. Radiologists’ mean sensitivity was 76% and mean specificity was 82%. At the high-sensitivity threshold, the TB AI was noninferior to average radiologist sensitivity (P<0.001) but not to average radiologist specificity (P=0.99) and was higher than the WHO target for specificity but not sensitivity. At the balanced threshold, the TB AI was comparable to radiologists. The abnormality AI’s sensitivity and specificity were 97% and 79%, respectively, with both meeting the prespecified targets.
Conclusions
The CXR TB AI was noninferior to radiologists for active pulmonary TB triaging in a population with a high TB and HIV burden. Neither the TB AI nor the radiologists met WHO recommendations for sensitivity in the study population. AI can also be used to detect other CXR abnormalities in the same population.
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