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Join us on Wednesday, September 17, 2025, from 4:00–5:00pm ET for a virtual seminar via Zoom, open to all internal and external participants. Monica Agrawal, PhD, Assistant Professor of Biostatistics & Bioinformatics at Duke University, will explore how natural language processing and large language models are transforming clinical text analysis to advance research, streamline physician workflows, and improve patient access to information. She will discuss scalable information extraction, smarter electronic health records, evaluation challenges for generative AI in medicine, and patient use of language models for health information.

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As Duke AI Health and the Pratt School of Engineering prepare to hold the second Duke AI for Health Innovation Summit in October (see Events section below), get inspired by the engagement and discussion of last year’s summit conference proceedings! The white paper available at the link below (PDF) captures key presentations, panel discussions, and informal “fireside chats.” READ MORE

Registration Open for Duke Summit on AI for Health Innovation: October 8-9, 2025!

“’The question is, again, what are the consequences of it?’ The answer, to him, rests in the stakes of making an error – and with healthcare, those stakes are serious.” The Verge’s Hayden Field interviews AI Health Director Michael Pencina, PhD, for an article that probes the implications of recent reports suggesting that a specialized medical large language model chatbot – Google’s Med-Gemini – may have hallucinated a nonexistent anatomical feature. The lapse, which made its way unrecognized into a preprint paper posted by Google, has raised concerns among AI researchers.

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Duke AI Health partners with investigators across the university on a wide range of impactful research. We’re excited to launch our new Project Profile feature, designed to spotlight this important work and showcase interdisciplinary innovation. Explore the latest profiles and learn more about these collaborations at the link below. READ MORE

A group of authors from Duke, including AI Health Data Science Fellow Elliot Hill and Data Science Fellowship Director Matthew Engelhard, published a research article that examined the propensity for a supervised machine learning approach known as binary classification to yield biased results when predicting long-horizon diagnoses. The paper, titled “Limitations of Binary Classification for Long-Horizon Diagnosis Prediction and Advantages of a Discrete-Time Time-to-Event Approach: Empirical Analysis,” was published in March in the journal JMIR AI.
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Duke Office of Information Technology Media Architect and Senior Producer Stephen Toback explores the capabilities and limitations of AI chatbots like ChatGPT in healthcare and academia. The session demonstrates real-world use cases ranging from clinical documentation to administrative support. Toback also addresses common pitfalls, such as hallucinations and privacy concerns, providing guidance on safe implementation. It’s a practical guide for institutions considering integrating generative AI tools. 

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Dr. Michael Cary, Duke AI Health faculty council member and Associate Professor and Elizabeth C. Clipp Term Chair of Nursing in the Duke University School of Nursing, was invited to speak at a congressional briefing titled Powered by Evidence: Quality Patient Care Requires Nursing Science. Hosted by the American Academy of Nursing (AAN) and the American Association of Colleges of Nursing (AACN), the event took place in Washington, DC and spotlighted the critical role of nursing science in improving patient care. Dr. Cary joined a distinguished panel that included nurse scientist Jeanne Alhusen, patient advocate Johane Joseph, and moderator Antonia Villarruel, Dean of the University of Pennsylvania School of Nursing. This national platform highlights Duke’s leadership in nursing research and health equity.

Researchers working with the Duke Center for Autism and Brain Development are conducting an innovative study that seeks to harness machine learning techniques to spot potential signs of autism in the electronic health data of young children.

“Duke data scientists, autism researchers, and healthcare professionals are collaborating to help pediatric providers optimize care for children with neurodevelopmental differences. By leveraging routinely collected health data, they aim to address a critical need for efficient, consistent, and objective methods for early autism screening.”

“’We know from previous work in the ACE [Autism Center of Excellence] that certain early childhood medical conditions can be indicative of a future autism diagnosis,’ said Benjamin Goldstein, PhD, director of data science at Duke AI Health and lead investigator on the study.”

–Story by Evan Watson, MLS

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Duke AI Health, the Duke Clinical Research Institute, and the Duke Center for Computational and Digital Health Innovation are thrilled to announce the second annual Duke Summit on AI for Health Innovation, that will take place October 8-9, 2025. This event aims to foster a vibrant community of practice that bridges the medical and engineering fields to advance health-oriented AI development. The summit will spotlight Duke’s expertise in AI product development and healthcare innovation, emphasizing the use of responsible AI to improve health outcomes for patients and communities. Please join us in shaping the future of AI for health!

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Dr. Sam Berchuck, AI Health Faculty affiliate and Assistant Professor of Biostatistics & Bioinformatics, presented recent work from the Duke Cancer Institute (DCI) Center for Onco-Primary Care funded project, “Characterizing pain trajectories in cancer survivors upon return to primary care using mixed methods,” at the DCI Cancer Prevention and Control (CPC) Seminar Series. The team also shared two posters at the CPC Group’s Spring Poster Fair, including one that was awarded Best Poster. These efforts highlight the integration of AI-driven longitudinal modeling and qualitative insights to better understand patterns of pain among breast cancer survivors.