About AI-HEART Lab
Physician-Built Technology to Accelerate Science and Transform Patient Care
Clinical Foundation
Rahul Chaudhary, MD, MBA, FACP, FACC
CEO, AI-HEART Lab, LLC
Practicing Cardiologist & Physician-Scientist-Executive-Engineer
Director of Product Management (Healthcare AI), Wolters Kluwer
VA Pittsburgh Healthcare System
AI-HEART Lab was founded by Dr. Rahul Chaudhary, a practicing cardiologist (FACP, FACC) and physician-scientist-executive-engineer. He is CEO of AI-HEART Lab, LLC, Director of Product Management (Healthcare AI) at Wolters Kluwer, and serves at the VA Pittsburgh Healthcare System.
- Johns Hopkins University — internal medicine residency, postdoctoral research in cardiovascular genetics
- Mayo Clinic Rochester — former assistant professor of medicine
- University of Pittsburgh Medical Center — cardiology fellowship, NIH T32 postdoctoral fellowship in AI-enhanced cardiovascular medicine
- Maulana Azad Medical College — MBBS (New Delhi)
- Indiana University Kelley School of Business — MBA, Lean Six Sigma Green Belt
- Georgia Institute of Technology — MSCS in machine learning (in progress)
- Wolters Kluwer — Director of Product Management (Healthcare AI)
Leadership
- CEO — AI-HEART Lab, LLC
- Director of Product Management (Healthcare AI) — Wolters Kluwer
- Practicing Cardiologist (FACP, FACC) — VA Pittsburgh Healthcare System
- Former Chief Medical Officer — AI health technology company
Research Impact
150+ peer-reviewed publications (h-index 27; 3,216 citations; Google Scholar, as of October 2026) spanning cardiovascular thrombosis, interventional cardiology, and AI applications in cardiovascular medicine. View full publication list.
- 18+ citations in clinical practice guidelines, scientific statements, and consensus documents — AHA, ESC, ACC/AHA, SCAI, HRS
- Guest Editor — Journal of Clinical Medicine, AI in Cardiovascular Procedures
- Chair, Endpoint Adjudication Committee — WARRIORS Trial, Imperial College London
- Peer review — Circulation, European Heart Journal, JACC Advances, npj Digital Medicine, BMJ Open
Technology Layer
The transition from pure clinical research to applied technology was deliberate — designed to bridge the gap between clinical expertise and engineering capability.
Education
- Georgia Institute of Technology — MSCS in machine learning (in progress)
- Indiana University Kelley School of Business — MBA, Lean Six Sigma Green Belt
- Executive education — Cornell, Wharton, MIT (AI systems, platform strategy, medical device development)
Products — Clinical Decision Support
- Clinical calculators — four free, guideline-aligned decision-support tools for practicing physicians: 2026 ACC/AHA Dyslipidemia, 2025 AHA/ACC Hypertension, 2022 AHA/ACC/HFSA Heart Failure, and 2023 ACC/AHA/ACCP/HRS Atrial Fibrillation — with AI-powered clinical note extraction, EHR note generation, and guideline-directed literature search
- COMPASS — Unified Clinical Decision Support — single-pass extraction and note generation across all four calculators with cross-guideline conflict resolution
- Shared Decision-Making — printable patient handouts with personalized risk visualization for statin and PCSK9 inhibitor therapy discussions
Products — Research Tools
- ARBITER — AI Pre-Submission Reviewer — physician-scientist calibrated pre-submission review that identifies methodological weaknesses, statistical gaps, and presentation issues before journal submission
- ARBITER Abstract — Pre-Submission Abstract Evaluation — abstract-level review for scientific conferences with structured feedback on methodology, presentation, and impact
- CANVAS — AI Scientific Illustration Generator — publication-quality graphical abstracts, mechanism diagrams, study flows, and results summaries from manuscript text
- CADENCE — AI Research Audiobook Generator — transform research papers and clinical guidelines into personalized audiobooks for learning during rounds, commute, or exercise
Products — Quality Assurance & Clinical Trials
- VIGIL — Clinical AI Audit — automated quality assurance for healthcare AI conversations and clinical documentation. Flags hallucinations, prompt injection, fabricated authorities, scope violations, and other clinically specific issues, with findings cross-checked across multiple models. Built on HIPAA-compliant infrastructure with zero data retention
- ENLIST — Clinical Trial Pre-Screening & EDC — AI-powered eligibility assessment and enterprise electronic data capture with 21 CFR Part 11 compliance, audit trails, e-signatures, and CDISC SDTM export
- LITMUS — AI Model Validation — independent sealed-sandbox validation of clinical AI models with signed, reproducible reports
Products — Medical & Patient Education
- MedStudentLearner — paste a clinical note, get pathophysiology diagrams, evidence cards, and flashcards for medical students and trainees
- Evidence Search — AI-powered clinical literature search across PubMed, Europe PMC, and Semantic Scholar with query refinement and evidence synthesis
- Education Digest — daily curated literature newsletter with auto-tagging by clinical theme across 16+ high-impact journals
In development: SENTINEL — AI-assisted endpoint adjudication for clinical trials
Vision
Clinical expertise is the scarcest input in health technology — and the most under-leveraged.
- The gap — most AI tools are built by engineers who consult clinicians, not clinicians who master engineering. Meanwhile, evidence-based guidelines take years to reach the patients who need them
- The mission — build physician-led technology that accelerates scientific discovery and closes the gap between published evidence and bedside care
- The standard — interpretable outputs, clinically meaningful endpoints, honest uncertainty — safe, effective, and timely
Learn more about our clinical validity, endpoint adjudication, and AI validation consulting services.
Connect
Research metrics, professional profile, and scheduling.
150+ publications, 3,216 citations, h-index 27 (Google Scholar, as of October 2026)
Professional profile
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