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This March, the Duke AI Health Community of Practice hosted the Machine Learning School: Health AI (MLSS-HealthAI), the 13th in the ML School Series since 2017. Machine learning expert Ricardo Henao, PhD, led the three-day program, which featured 10 expert presenters from across Duke. The event brought together 149 participants representing 58 organizations, including 56 students and 22 scholarship recipients (in keeping with its educational mission, the Community of Practice reserved a minimum of 10% of seats for scholarships to support attendance by those who would otherwise be unable to attend).

A major highlight of this year’s event was the hands-on use of the Duke Health AI Studio, which offers a variety of AI platforms for Duke users in a protected environment. With help and expert guidance from Duke Health Technology Solutions (DHTS), participants were able to explore real-world examples, and apply new techniques in an immersive computing environment.

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In February, a select steering committee representing multiple schools, institutes and disciplines across Duke University published a report on artificial intelligence to the Duke University Provost. The report synthesized perspectives on Duke’s strengths in AI as well as gaps and opportunities, including the potential for improving institutional approaches to developing, applying, and providing ethical oversight for these technologies at the university. The Steering Committee, whose leadership included Duke AI Health Faculty Council Member Ricardo Henao, PhD, as a co-chair, also examined infrastructural needs for AI at Duke and articulated a strategic vision for the future of human-centered AI in research and education.

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On March 29, Duke University Executive Vice President for Health Affairs and School of Medicine Dean Mary E. Klotman, MD, announced that Duke Clinical Research Institute Director of Data Science Christopher Lindsell, PhD, has been appointed to serve as Vice Dean for Data Science & AI at the Duke University School of Medicine, effective April 1. In this new role, Dr. Lindsell will lead the strategy, execution, and governance of AI and data science across the School of Medicine while collaborating closely with key partners in the Duke University Health System and the university. His immediate priorities include advancing a unified strategy for AI research, education, and workforce training, as well as building multidisciplinary teams, coordinating platform development and oversight, and accelerating our learning health system to ensure that discovery, clinical decision‑making, and operations at Duke are informed by the highest-quality data possible. READ MORE

Please help us make AI Health’s Community of Practice better! In this brief survey, we are trying to get a sense of what AI Health offerings you may have found useful or valuable over the past year. Have you participated in any of our classes or seminars? Have you attended one of our conferences or summits? Are you enjoying the Friday Roundup? Are there any ways we can improve or serve you better?

Take the survey here: https://duke.qualtrics.com/jfe/form/SV_3sCUufSFTxCiMsu

The responses from last year’s impact survey were extremely helpful in our planning for this year’s offerings.  We are genuinely interested in what you have to say, and what we can do to improve on what we are doing! Check out last year’s results at https://duke.is/2025-ai-health-survey-summary

The linked Qualtrics survey is anonymous and should only take a few minutes to complete. As an added incentive, participants who complete their surveys may choose to enter a raffle for Duke AI Health mugs and stickers.  We very much appreciate your feedback!

Duke AI Health Program Coordinator Tiffany Torres recently earned her SHRM-CP (Society for Human Resource Management–Certified Professional) credential. This nationally recognized certification reflects advanced expertise in people operations, workplace policies, and employee support—capabilities that strengthen internal team performance while enhancing collaboration with external partners and sponsors. Her accomplishment underscores a commitment to supporting effective, well-coordinated teams and the people-centered practices that enable Duke AI Health’s work at the intersection of AI and health. Please join us in congratulating Tiffany on this achievement!

A paper co-authored by Duke AI Health researchers Christine Shen, Christian Péan,MD, and Samuel Berchuck, PhD, received a Student Paper Award from the Bayesian Statistical Science Section of the American Statistical Association. The work introduces a new spatial modeling approach to identify geographic patterns in hospital readmission risk following upper extremity fracture surgery while accounting for competing mortality risk. Using high-resolution maps, the team highlights local areas where patients face elevated risk, creating opportunities for targeted, community-level interventions. This framework demonstrates how advanced statistical methods can translate complex health data into actionable insights for population health and care delivery.

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The Duke AI Health Community of Practice is pleased to host the Duke Machine Learning Spring School 2026: Health AI (MLSS-HealthAI), offered in March as an in-person three-day class that will provide perspective and insight on the fundamentals of generative artificial intelligence methods and applications. The curriculum in the MLSS-HealthAI is targeted to individuals interested in learning about generative AI with health applications and will introduce the mathematics and statistics at the foundation of current generative and representation learning models, plus provide context for the methods that have formed the foundations of rapid growth in AI. Additionally, the MLSS-HealthAI will provide hands-on experience during practical sessions that illustrate the use of such methods and applications. LEARN MORE

In June 2025, scientific collaborators from Duke AI Health and the American Heart Association, led by principal investigator and former AI Health Director Michael Pencina, PhD, concluded a five-year study funded by the National Institutes of Health and designed to improve the ability to predict a patient’s risk of stroke. After aggregating and harmonizing patient-level data collected from four NIH-sponsored observational cohort studies, the study investigators evaluated the performance of existing stroke risk prediction models, developed new models with the potential to improve clinical decision-making, and explored ways to mitigate algorithmic bias and improve the fairness of models in clinical use. Key findings from the study have been shared through 18 publications in high-impact academic journals and conference proceedings, and the code used to conduct analyses is publicly available through the study’s dedicated GitHub repository.

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This fall, the Duke AI Health Community of Practice successfully concluded its third Scientific Writing Workshop for Staff. This four-unit minicourse is designed to offer a wide-ranging introduction to scientific writing and peer-reviewed publication. AI Health Research Scientist Whitney Welsh, PhD, has created an impact report (linked below) that provides a detailed snapshot of how this most recent iteration of the program was received. In December, workshop participants presented the research posters they developed as a key class project at the 2025 Health Data Science Poster Showcase. Congratulations and thanks to all of our workshop participants for making the fall 2025 offering such a success!

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This December, Duke AI Health partnered with the Pratt School of Engineering, the Duke Center for Computational Thinking, the Duke Center for Computational and Digital Health Innovation, and Duke Health to present the fourth Data Science Poster Showcase. The Showcase is a recurring semiannual event designed to provide a forum that allows Duke staff, students, trainees, and faculty to highlight their work, research, or class projects, while also gaining experience in presenting their projects to a larger audience. This year’s event encompassed over 60 posters on topics that included statistics, biomedical informatics, epidemiology, machine learning, implementation science, process engineering, quality improvement, among many others.

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