Rohan Khera, MD, MS
Cardiologist · Data Scientist · Yale University
Improving cardiovascular outcomes through machine learning, clinical informatics, and AI-driven precision medicine.
About
I am a cardiologist and data scientist at Yale University, where I direct the Cardiovascular Data Science (CarDS) Lab. My work sits at the intersection of clinical medicine and computational methods—using electronic health records, electrocardiography, cardiovascular imaging, and wearable devices to build tools that improve how care is delivered to patients with or at risk for heart disease.
My research develops and validates machine learning and AI approaches that translate large-scale digital health data into actionable clinical insights. A central aim is precision cardiovascular care: identifying the right patients, the right interventions, and the right timing—at scale, and in populations historically underserved by conventional risk tools.
I trained in medicine at the All India Institute of Medical Sciences, completed internal medicine residency at the University of Iowa, and cardiology fellowship and postdoctoral research training at UT Southwestern Medical Center, where I also earned a Master of Clinical Sciences. I serve as Associate Editor for Artificial Intelligence and Digital Health at JAMA.
Recognition
My work has been recognized with the Douglas P. Zipes Distinguished Young Scientist Award (ACC, 2026), the Young Physician-Scientist Award (ASCI, 2023), the Blavatnik Award for Innovation (2023), the Doris Duke Clinician Scientist Development Award (2022), and the Jeremiah Stamler Distinguished Young Investigator Award (AHA, 2021). I am a Fellow of the American College of Cardiology (FACC) and the American Heart Association (FAHA), and board-certified in Cardiovascular Disease and Clinical Informatics. I have served as Special Advisor for AI to the American College of Cardiology and on international scientific advisory boards including the British Heart Foundation Centre of Excellence at King's College London and the Friede Springer Cardiovascular Prevention Center at Charité Berlin.
Research Focus
AI in Cardiovascular Imaging
Deep learning applied to echocardiography and cardiac imaging for automated diagnosis, prognostication, and disease screening at scale.
ECG-Based Phenotyping
Neural network models that extract latent phenotypes from the 12-lead and single-lead ECG to detect structural heart disease, cardiomyopathy, and arrhythmia risk.
Clinical NLP & EHR Science
Large language models and structured EHR data to characterize disease burden, treatment patterns, and quality of care at population scale.
Wearable & Remote Monitoring
Machine learning on continuous wearable data for passive detection of cardiac events and monitoring of patients with heart failure.
Health Equity & Global Health
Designing and auditing AI models for fairness across populations; extending validated tools to low- and middle-income country contexts.
Precision Therapeutics
Real-world evidence and computational phenomapping for personalized inference from clinical trials to diverse patient populations.
Roles & Affiliations
- Associate Professor of Medicine (Cardiology) & Biostatistics Yale School of Medicine
- Director, Cardiovascular Data Science (CarDS) Lab Yale University
- Clinical Director, Center for Health Informatics & Analytics Yale-New Haven Hospital CORE
- Associate Editor, AI & Digital Health JAMA
- Attending Cardiologist Yale New Haven Health
Selected Work
A representative sample. Full list on Google Scholar and PubMed.
- Complete AI-Enabled Echocardiography Interpretation With Multitask Deep Learning
- AI-Enabled ECG and Echocardiography to Track Preclinical Progression of Transthyretin Amyloid Cardiomyopathy
- Identification of Hypertrophic Cardiomyopathy on Electrocardiographic Images With Deep Learning
- AI-Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms
- Ensemble Deep Learning for Structural Heart Disease Screening Using ECG Images: PRESENT SHD
- TARGET-AI: A Foundational Approach for Targeted Deployment of AI-ECG in the EHR
Consulting & Advisory
I advise healthcare technology companies, medical device manufacturers, pharmaceutical firms, health systems, and investment firms on the development, validation, and responsible deployment of AI tools in cardiovascular and broader clinical settings. I bring both the scientific rigor of academic research and the clinical perspective of a practicing cardiologist.
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