Overview
Michael J. Pencina, PhD
Chief Data Scientist, Duke Health
Vice Dean for Data Science
Director, Duke AI Health
Professor, Biostatistics & Bioinformatics
Duke University School of Medicine
Michael J. Pencina, PhD, is Duke Health's chief data scientist and serves as vice dean for data science, director of Duke AI Health, and professor of biostatistics and bioinformatics at the Duke University School of Medicine. His work bridges the fields of data science, health care, and AI, contributing to Duke’s national leadership in responsible health AI.
Dr. Pencina partners with key leaders to develop data science strategies for Duke Health that span and connect academic research and clinical care. As vice dean for data science, he develops and implements quantitative science strategies to support the School of Medicine’s missions in education and training, laboratory and clinical science, and data science.
He co-founded and co-leads the national Coalition for Health AI (CHAI), a multi-stakeholder effort whose mission is to increase trustworthiness of AI by developing guidelines to drive high-quality health care through the implementation of innovative, credible, and transparent health AI systems. He serves in a leadership capacity for the Trustworthy & Responsible AI Network (TRAIN), a new organization Duke co-founded with leading health care and technology organizations to develop tools and technologies that promote the adoption of high-quality, novel, and safe health AI solutions for patient care and research. He also spearheaded establishing and co-chairs Duke Health’s Algorithm-Based Clinical Decision Support (ABCDS) Oversight Committee.
Dr. Pencina is an internationally recognized authority in the evaluation of AI algorithms. Guideline groups rely on his work to advance best practices for the application of clinical decision support tools in health delivery. He interacts frequently with investigators from academic and industry institutions as well as government officials. Since 2014, Thomson Reuters/Clarivate Analytics has regularly recognized Dr. Pencina as one of the world’s "highly cited researchers" in clinical medicine and social sciences, with more than 400 publications cited over 135,000 times. He serves as a deputy editor for statistics at JAMA-Cardiology.
Dr. Pencina joined the Duke University faculty in 2013, and served as director of biostatistics for the Duke Clinical Research Institute until 2018. Previously, he was an associate professor in the Department of Biostatistics at Boston University and the Framingham Heart Study, and director of statistical consulting at the Harvard Clinical Research Institute. He received his PhD in Mathematics and Statistics from Boston University in 2003 and holds master’s degrees from the University of Warsaw in actuarial mathematics and business culture.
Email: michael.pencina@duke.edu
Web Sites: medschool.duke.edu; aihealth.duke.edu; https://scholars.duke.edu/person/michael.pencina
Phone: 919.613.9066
Address: Duke University School of Medicine; 2424 Erwin Road, Suite 903; Durham, NC 27705
Current Appointments & Affiliations
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Recent Publications
Exploring trade-offs in equitable stroke risk prediction with parity-constrained and race-free models.
Journal Article Artif Intell Med · June 2025 A recent analysis of common stroke risk prediction models showed that performance differs between Black and White subgroups, and that applying standard machine learning methods does not reduce these disparities. There have been calls in the clinical litera ... Full text Link to item CiteHypertensive disorders of pregnancy and gestational diabetes mellitus and predicted risk of maternal cardiovascular disease 10-14 years after delivery: A prospective cohort.
Journal Article Diabet Med · May 2025 AIMS: Studies evaluating the relationship between adverse pregnancy outcomes (APOs), namely hypertensive disorders of pregnancy (HDP) and gestational diabetes mellitus (GDM), with the estimated risk of atherosclerotic cardiovascular disease (ASCVD) remains ... Full text Link to item CiteApplication of unified health large language model evaluation framework to In-Basket message replies: bridging qualitative and quantitative assessments.
Journal Article J Am Med Inform Assoc · April 1, 2025 OBJECTIVES: Large language models (LLMs) are increasingly utilized in healthcare, transforming medical practice through advanced language processing capabilities. However, the evaluation of LLMs predominantly relies on human qualitative assessment, which i ... Full text Link to item CiteRecent Grants
Leveraging machine learning for cardiovascular disease risk prediction and prevention in women with a history of adverse pregnancy outcomes
ResearchPrincipal Investigator · Awarded by Brigham and Women's Hospital · 2023 - 2027HPV, HIV and Oral Microbiota Interplay in Nigerian Youth (HOMINY)
ResearchCollaborator · Awarded by University of Pennsylvania · 2024 - 2027Improving Racial Equity in Clinical Decision Making about Access to Organ Transplant
ResearchMentor · Awarded by National Institute on Minority Health and Health Disparities · 2022 - 2027View All Grants