We’re excited to announce the launch of the Protected Research Compute Cluster (PRCC), a high-performance, secure environment for research involving PHI and other sensitive data. PRCC offers customizable workspaces with familiar tools, optimized GPU and storage capacity, and simplified access to Duke Health’s data resources. Replacing PACE, PRCC expands capacity and reliability while enabling global collaboration, all within a NIST 800-53 compliant framework. Together with the Research Computing Cluster (RCC), PRCC forms part of the Duke University School of Medicine Research Enclave, giving researchers flexible, scalable options for secure data analysis.

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Andrew Olson, Associate Director of Policy Strategy and Solutions for Health Data Science at Duke AI Health, will present for the Office of Physician-Scientist Development’s Research Careers Ahead Virtual Seminar Series in a virtual seminar via Zoom on Wednesday, October 22, 2025, from 4:00–5:00pm ET. His talk, “Clinical Risk Prediction Modeling with Machine Learning and AI,” will explore how these methods are being applied to improve health outcomes.

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Patients are increasingly using generative AI to answer health questions, through tools like chatbots or AI-powered search results. Recent research led by Monica Agrawal, PhD, AI Health Faculty Affiliate and Assistant Professor of Biostatistics & Bioinformatics, characterizes the potential failure modes of this phenomenon, analyzes how LLM-generated responses can mislead patients even without hallucinations, and offers recommendations for building safer systems. The paper, “Retrieval-augmented systems can be dangerous medical communicators,” was presented in July at the International Conference on Machine Learning (ICML).

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We invite you to a virtual seminar on Tuesday, September 23, 2025, from 12:00–1:00pm ET for a virtual seminar via Zoom, open to all internal and external participants. Xiaoyue Ni, PhD, Assistant Professor of Mechanical Engineering & Materials Science and Biostatistics & Bioinformatics at Duke University, will present on a soft, skin-mounted mechano-acoustic (MA) sensing platform that records body sounds and kinematics with high fidelity. This technology leverages epidermal electronics to capture a high-dimensional array of mechanical and acoustic signatures, enabling comfortable, accurate, and comprehensive decoding of physiological states, behavioral patterns, functional performance, and cognitive or intentional states in real time.

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We invite you to attend a virtual seminar taking place on Thursday, October 2 from 12:00 noon to 1:00 PM Eastern, where Duke AI Health Faculty Affiliate Chuan Hong, PhD, will share findings from a large-scale evaluation of the American Heart Association’s PREVENT model for predicting 10-year cardiovascular risk. She will discuss the model’s performance across diverse populations and the impact of social determinants of health, highlighting both its strengths and key disparities. Her talk will consider the implications for applying PREVENT in real-world clinical settings. The seminar is free and open to the public.

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Clinical notes often contain important descriptive findings not captured in structured EHR fields, making them valuable for early autism prediction. However, identifying autism-related insights is difficult due to their sparsity within the large volume of notes for a typical child. Duke researchers, including Computational Biology & Bioinformatics student Fengnan Li, AI Health Data Science Fellow Elliot Hill, and Duke AI Health Data Science Fellowship Director Matthew Engelhard, PhD have developed a new natural language processing method, IRIS (Interpretable Retrieval-Augmented Classification for long Interspersed Document Sequences), to address this challenge. Their work was recently published at the 2025 Annual Meeting of the Association for Computational Linguistics.

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AI Health Director of Data Science Ben Goldstein, PhD, and AI Health Faculty Affiliate Matt Engelhard, MD, PhD, will be presenting papers at the upcoming Machine Learning for Healthcare (MLHC) conference at the Mayo Clinic in Rochester, MN. The first paper, Borrowing from the Future: Enhancing Early Risk Assessment through Contrastive Learning (Sun, Engelhard, Goldstein), explores improved early risk prediction using contrastive learning methods. The second, Balancing Interpretability and Flexibility in Modeling Diagnostic Trajectories with an Embedded Neural Hawkes Process Model (Zhao, Engelhard), investigates modeling approaches that support both transparency and complexity in clinical data. Duke AI Faculty Council Member Ricardo Henao, PhD, will also be presenting a poster with colleague Mohd Ashhad titled Generating Accurate Synthetic Survival Data by Conditioning on Outcomes. LEARN MORE

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!