Qingyu Chen, PhD
Assistant Professor, Yale University
AI in medicine: developed, evaluated, and trusted.
Biomedical NLP · Medical LLMs · Multimodal AI · Trustworthy AI for medicine
Our lab develops AI for medicine across language, imaging, and multimodal health data. We focus on medical LLMs and biomedical NLP, foundation and multimodal models for biomedical and healthcare applications, ophthalmology and medical imaging AI, and rigorous evaluation for trustworthy use in real-world healthcare.
We develop and evaluate large language models for biomedical and clinical text. Our work includes the development of domain-specific LLMs and broad applications in biomedical natural language processing.
We build foundation and multimodal models that integrate language, medical images, and other health data. Our goal is to support scalable representation learning and clinically relevant prediction across medical tasks.
We develop AI methods for ophthalmology and broader medical imaging applications, including disease screening, diagnosis, prognosis, and multimodal reasoning. We are especially interested in open and clinically useful models for eye care.
We study how AI systems in medicine should be evaluated, validated, and trusted. Our work focuses on factuality, memorization, generalization, real-world evaluation, and responsible deployment in healthcare.
Principal Investigator supported by NIH R01, NIH R00, the Blavatnik Fund, Google Research, and Microsoft Azure
45+ first- or last-authored papers out of 110+ publications
8,300+ citations and an h-index of 46 as of May 2026
Awards include the Google Research Scholar Award, AMIA Distinguished Paper Award, NIH Fellows Award for Research Excellence, NLM Honor Award, and Teaching Excellence Award
Publications in Nature, Nature Medicine, Nature Communications, Nature Aging, npj Digital Medicine, Nucleic Acids Research, The Lancet Digital Health, and others
We are a growing interdisciplinary lab working across biomedical NLP and LLMs, multimodal AI, medical imaging, and trustworthy AI in medicine.
We welcome prospective students, postdocs, and collaborators who are interested in building rigorous, clinically grounded, and impactful AI for biomedicine and healthcare.