Qingyu Chen, PhD
Assistant Professor of Biomedical Informatics and Data ScienceDownloadHi-Res Photo
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Ophthalmology
Secondary
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Assistant Professor of Biomedical Informatics and Data Science
Biography
We have open positions and welcome inquiries from talented and motivated students, postdocs, and collaborators. Please read this page before emailing me so that we can quickly determine whether there may be a good fit.
Dr. Qingyu Chen is a tenure-track Assistant Professor in the Department of Biomedical Informatics & Data Science at Yale School of Medicine, with a secondary appointment in Ophthalmology. His research focuses on artificial intelligence in biomedicine and healthcare, with interests spanning biomedical natural language processing and large language models, medical imaging and multimodal analysis, and trustworthy AI for medical applications. He is the Principal Investigator of an NIH R01 grant on improving the factuality and reasoning of large language models in medicine (see news) and an NIH K99/R00 grant on multimodal AI-assisted disease diagnosis (see news). He has published over 50 first- or last-authored papers among more than 110 total publications, with over 8,300 citations and an h-index of 46 as of May 2026, in journals including Nature, Nature Medicine, Nature Machine Intelligence, Nature Aging, npj Digital Medicine, and Nucleic Acids Research, among others. Before joining Yale in 2024, he completed postdoctoral training at the National Library of Medicine, NIH, and received his PhD in Computer Science (Biomedical Informatics) from the University of Melbourne. He is also a committed educator and mentor who has taught more than 20 courses and mentored over 20 trainees, and has received the NIH Research Mentor Award and Excellence in Teaching Awards.
For more information, please visit https://www.qingyuchen-lab.com.
Last Updated on May 17, 2026.
Appointments
Biomedical Informatics & Data Science
Assistant ProfessorPrimaryOphthalmology
Assistant ProfessorSecondary
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Education & Training
- PhD
- University of Melbourne, Computer Science and Biomedical Informatics (Microsoft Innovation Award; Excellence in Teaching Award; Top-ranked performance in AI challenge tasks
- BS (Hon)
- The Royal Melbourne Institute of Technology, Computer Science (GPA ranked 1st; First-class Honor; Academic Excellence Award) (2013)
Research
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Overview
Our research focuses on the development, evaluation, and trustworthy use of AI in medicine. We work across language, imaging, and multimodal health data, with particular interests in biomedical NLP, medical LLMs, multimodal and foundation models, ophthalmology and medical imaging AI, and rigorous evaluation for real-world applications in biomedicine and healthcare. For more information, please visit our Research page.
ORCID
0000-0002-6036-1516- View Lab Website
Qingyu Chen Lab
Research at a Glance
Yale Co-Authors
Frequent collaborators of Qingyu Chen's published research.
Publications Timeline
A big-picture view of Qingyu Chen's research output by year.
Hua Xu, PhD
Vipina K. Keloth, PhD
Ron Adelman, MD, MPH, MBA, FACS
Huan He, PhD
Kalpana Raja, PhD, MRSB, CSci
Andrew Taylor, MD, MHS
103Publications
5,063Citations
Publications
2026
LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology
Qin Z, Liu Y, Yin Y, Ding J, Zhang H, Li A, Campbell D, Wu X, Zou K, Keenan T, Chew E, Lu Z, Tham Y, Liu N, Zhang X, Chen Q. LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology. ACM Transactions On Computing For Healthcare 2026 DOI: 10.1145/3801746.Peer-Reviewed Original ResearchCitationsAltmetricConceptsArtificial intelligenceCuration pipelineZero-shot settingDisease diagnosis tasksFree-text annotationsState-of-the-artMedical image interpretationOphthalmic disease diagnosisDiabetic retinopathy classificationColor fundus photographyBenchmark datasetsDemographic predictionsLanguage modelMultimodal datasetDiagnosis tasksRandom baselineMLLMGenerative modelRetinopathy classificationDataset websiteComprehensive benchmark datasetNarrow tasksFine-tuningDatasetDisease labelsContext for Large Language Model Evaluation in Ophthalmology—Reply
Srinivasan S, Chen Q, Tham Y. Context for Large Language Model Evaluation in Ophthalmology—Reply. JAMA Ophthalmology 2026, 144: 288-288. PMID: 41569551, DOI: 10.1001/jamaophthalmol.2025.5847.Peer-Reviewed Original ResearchAgentic artificial intelligence in eye care: is clinical autonomy finally within reach?
Zou K, Lin Goh J, Yang G, Srinivasan S, Chen Q, Keane P, Tham Y. Agentic artificial intelligence in eye care: is clinical autonomy finally within reach? The Lancet Digital Health 2026, 100967. PMID: 41720669, DOI: 10.1016/j.landig.2025.100967.Peer-Reviewed Original ResearchCitationsAltmetricReasoning-driven large language models in medicine: opportunities, challenges, and the road ahead
Wang X, Xiong Z, Zou K, Srinivasan S, Lo T, Wu Y, Zou M, Liu N, Antaki F, Ma W, Atanasov A, Savulescu J, Car J, Klonoff D, Sheng B, Wong T, Chen Q, Tham Y. Reasoning-driven large language models in medicine: opportunities, challenges, and the road ahead. The Lancet Digital Health 2026, 8: 100931. PMID: 41620322, DOI: 10.1016/j.landig.2025.100931.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsInformation extraction from clinical notes: are we ready to switch to large language models?
Hu Y, Zuo X, Zhou Y, Peng X, Huang J, Keloth V, Zhang V, Weng R, Shyr C, Chen Q, Jiang X, Roberts K, Xu H. Information extraction from clinical notes: are we ready to switch to large language models? Journal Of The American Medical Informatics Association 2026, 33: 553-562. PMID: 41533750, PMCID: PMC12981642, DOI: 10.1093/jamia/ocaf213.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsGraphics processing unitsInformation extractionClinical NERComputational costClinical information extractionBidirectional Encoder RepresentationsComputational efficiencyEntity recognitionBERT modelEncoder RepresentationsLanguage modelRE tasksI2b2 datasetMedical Information Mart for Intensive CareF1 scorePerformance gainsBERTIE applicationsAI modelsProcessing unitClinical notesComputational demandsNERResource constraintsData conditionsAMD-Mamba: A Phenotype-Aware Multi-modal Framework for Robust AMD Prognosis
Wu P, Lin M, Chen Q, Chew E, Lu Z, Peng Y, Dong H. AMD-Mamba: A Phenotype-Aware Multi-modal Framework for Robust AMD Prognosis. Lecture Notes In Computer Science 2026, 16241: 150-160. PMID: 41527550, PMCID: PMC12790706, DOI: 10.1007/978-3-032-09513-8_15.Peer-Reviewed Original ResearchCitationsConceptsMulti-modal frameworkAge-related macular degenerationMulti-scale fusionMetric learning strategyAMD biomarkersCNN backboneProgression of age-related macular degenerationColor fundus imagesPresence of drusenColor fundus photographsImage informationIrreversible vision lossLocal informationGenetic variantsFundus imagesAMD patientsFundus photographsMacular degenerationVision lossExperimental resultsSeverity Scale scoreClinical phenotypeClinical variablesLearning strategiesVascular changes
2025
BEnchmarking Large Language Models for Ophthalmology (BELO): An Expert-Curated Data Set and Evaluation Framework for Knowledge and Reasoning
Srinivasan S, Ai X, Lo T, Gilson A, Zou M, Zou K, Kim H, Yang M, Pushpanathan K, Yew S, Loke W, Goh J, Chen Y, Kong Y, Fu E, Ong M, Nwanyanwu K, Dave A, Li K, Sun C, Chia M, Yang G, Wong W, Chen D, Liu D, Singer M, Antaki F, Del Priore L, Jonas J, Adelman R, Chen Q, Tham Y. BEnchmarking Large Language Models for Ophthalmology (BELO): An Expert-Curated Data Set and Evaluation Framework for Knowledge and Reasoning. Ophthalmology Science 2025, 6: 101050. PMID: 41696659, PMCID: PMC12906013, DOI: 10.1016/j.xops.2025.101050.Peer-Reviewed Original ResearchConceptsLanguage modelBidirectional Encoder RepresentationsMedical data setsMacro F1-scoreData setsExpert checksStandard evaluation benchmarksQuestion AnsweringKeyword matchingMacro-F1Encoder RepresentationsReasoning capabilitiesHuman expertsPublic leaderboardEvaluation benchmarkText-generationManagement tasksModel performanceHigher accuracyBenchmarking effortsTransformation modelEvaluation frameworkMultiple roundsRelevant benchmarksBenchmarksPerformance of GPT-5 Frontier Models in Ophthalmology Question Answering
Antaki F, Mikhail D, Milad D, Mammo D, Sharma S, Srivastava S, Chen B, Touma S, Sevgi M, El-Khoury J, Keane P, Chen Q, Tham Y, Duval R. Performance of GPT-5 Frontier Models in Ophthalmology Question Answering. Ophthalmology Science 2025, 6: 101034. PMID: 41552655, PMCID: PMC12811449, DOI: 10.1016/j.xops.2025.101034.Peer-Reviewed Original ResearchCitationsConceptsQuestion AnsweringAccuracy-cost trade-offAdvanced reasoning capabilitiesQuestion-answering datasetAutomated evaluationQuestion-answering taskCost-accuracy trade-offReasoning capabilitiesLanguage modelBradley-TerryReasoning modelTrade-OffsDesign evaluationHigher accuracyHigh-performance configurationDatasetAccuracyCost-accuracyScience coursesPareto frontierHuman participantsOpenAIPerformanceAutograderEvaluation of diagnostic testsAgentMD: Empowering language agents for risk prediction with large-scale clinical tool learning
Jin Q, Wang Z, Yang Y, Zhu Q, Wright D, Huang T, Khandekar N, Wan N, Ai X, Wilbur W, He Z, Taylor R, Chen Q, Lu Z. AgentMD: Empowering language agents for risk prediction with large-scale clinical tool learning. Nature Communications 2025, 16: 9377. PMID: 41130954, PMCID: PMC12549800, DOI: 10.1038/s41467-025-64430-x.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsLanguage agentsHealthcare analyticsUnit testsTool learningTool buildersEmergency department notesDissemination challengesClinical calculatorsDiverse setRisk predictionIndividual patient careQuality checksUsabilityUsersPatient careMedical riskLearningCheckingAccuracyRisk managementClinical contextIntegrating rule-based NLP and large language models for statin information extraction from clinical notes
Liu S, McCoy A, Chen Q, Wright A. Integrating rule-based NLP and large language models for statin information extraction from clinical notes. International Journal Of Medical Informatics 2025, 205: 106104. PMID: 40925145, PMCID: PMC12705237, DOI: 10.1016/j.ijmedinf.2025.106104.Peer-Reviewed Original ResearchCitationsConceptsNatural language processingClinical decision supportAI frameworkArtificial intelligenceDecision supportRule-based natural language processingMulti-category classifierHybrid artificial intelligenceClinical notesInformation extractionLanguage modelClinical decision support toolLanguage processingTrustworthy solutionRefinement filterPatient-level insightsAdherence to clinical guidelinesPatient-level barriersDatasetManual chart reviewAcademic medical centerFrameworkInformationProvider burdenCardiovascular care
Academic Achievements & Community Involvement
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Honors
honor National Library of Medicine Honor Award
12/31/2023National AwardNational Institutes of Healthhonor National Library of Medicine Data Science and Informatics Mentor Awards
07/01/2023National AwardNational Institutes of HealthDetailsUnited Stateshonor Summer Research Mentor Award
07/01/2023National AwardNational Institutes of Healthhonor Fellows Award for Research Excellence
05/01/2023National AwardNational Institutes of Healthhonor Summer Research Mentor Award
07/01/2022National AwardNational Institutes of Health
News
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News
- February 28, 2025Source: Yale Ventures
YSM Faculty Win Awards From the Blavatnik Fund for Innovation at Yale
- October 02, 2024
NIH Awards $1.5 Million Grant to Improve Factual Correctness in Large Language Models in Health Care
- October 02, 2024
Advancing AI-Assisted Diagnosis of Ophthalmic Diseases
- October 02, 2023
What Does Natural Language Processing Mean for Biomedicine?
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May 202625Monday
- EveryoneTENTATIVESpeakers to be announced.