Our 2025 Duke AI Health Friday Roundup series continued to offer a weekly collection of notable news and views on AI, clinical research, basic science, and more, drawn from the scholarly literature, tech reporting, and broader media.

In December, Duke AI Health’s Community of Practice, in partnership with the Duke Pratt School of Engineering, the Duke Center for Computational and Digital Health Innovation, and Duke Health, hosted the 2025 Health Data Science Poster Showcase. The event, which was open to Duke students, trainees, staff, and faculty, also included participants in the Fall 2025 AI Health Foundations of Scientific Writing for Staff Workshop.

This fall, the AI Health Community of Practice held its third Scientific Writing Workshop for Duke staff members. This interactive hybrid mini-course, held over multiple sessions in October and November, introduced learners to the process of writing for scholarly publication and culminated in an in-person poster presentation that was part of the December Health Data Science Poster Showcase.

October of 2025 saw a successful second iteration of the annual Duke Summit on AI for Health Innovation, co-sponsored by Duke AI Health, the Duke Center for Computational and Digital Health Innovation, and the Duke Clinical Research Institute. This year’s event took place over two days and was hosted at the NC Biotechnology Center in Research Triangle Park. You can read more about the Summit in our Impact Report.

The 2025 Medical Imaging & AI Proposal Studios selected three outstanding proposal concepts from new investigators for intensive scientific feedback and design support. During November studio sessions, investigators collaborated with some of Duke’s leading data-science experts to strengthen their proposals for upcoming high-impact funding opportunities.

Earlier this year, Duke’s Collaborative to Advance Health Equity (CACHE) Initiative awarded 1-year grants for three proposals to improve care in the Duke Health system. Focused on addressing areas where current approaches to care and support could be improved, the three projects include identification and treatment of chronic kidney disease, transitions of care for patients with opioid use disorder, and improving screening for lung cancer.

A story published by the Duke Center for Autism and Brain Development highlights work by Duke AI Health Director of Data Science Ben Goldstein, PhD, and colleagues, whose AURORA study is using machine learning to sift electronic health records to better identify and understand autism in children.

This past summer saw the return of the popular Machine Learning Summer School program led by AI Health Faculty Council member Ricardo Henao, PhD, with offerings focused on generative AI.

A Duke AI Health Faculty Affiliate Monica Agrawal, PhD, was among this year’s honorees when the 2025 Whitehead Scholars were announced by the Duke University School of Medicine in May. The program recognizes and supports outstanding potential in early-career biomedical researchers.

In May of 2025, the Patient-Centered Outcomes Research Institute awarded funding to a Duke Health program designed to implement electronic monitoring of patients’ self-reported symptoms during cancer treatment. AI Health Advisory Board Member Richard Shannon, who also serves as chief medical officer and chief quality officer for Duke Health, and Duke hematologist/oncologist Thomas LeBlanc, are leading the study.

AI Health Data Science Fellowship Director Matthew Engelhard, PhD (R) is featured in a Duke story reporting on a model developed by Dr. Jonathan Posner, former AI Health Data Science Fellow Elliot Hill, and Engelhard at Duke that uses machine learning to predict mental health risks in adolescents. The project is supported by a $15 million grant from the National Institute of Mental Health.

In January of 2025, former Duke AI Health Director Michael Pencina, PhD (L) is interviewed by JAMA + AI editor Roy Perlis and discusses his recent publication on AI tracking tools for transparency and collaboration. Also in January, Duke AI Health Faculty Council member Michael Cary, PHD, RN (R) is featured in Duke Magazine.

Duke AI Health Director of Data Science Ben Goldstein, PhD, is among the authors of a new retrospective cohort study that examines relationships between patient outcomes during recovery from traumatic brain injuries and a set of social determinants of health, assessed at the level of the neighborhood environment. The article, titled “Association of neighborhood disadvantage with clinical and healthcare utilization outcomes following traumatic brain injury,” is available online ahead of print from the Journal of Clinical Neuroscience.

People are increasingly seeking healthcare information from large language models (LLMs) via interactive chatbots, yet the nature and inherent risks of these conversations remain largely unexplored. Recent research led by Monica Agrawal, PhD, a Duke AI Health faculty affiliate, releases HealthChat-11K, a curated dataset of 11K real-world chatbot conversations in which users seek healthcare information. This dataset can be used to analyze user interactions, including dangerous interactions with the potential to induce sycophancy in LLMs. The paper, titled “‘What’s Up, Doc?’: Analyzing How Users Seek Health Information In Large-Scale Conversational AI Datasets”, was presented in November as a Findings paper at the Conference on Empirical Methods in Natural Language Processing (EMNLP).

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In a new research article titled “Using mixture cure models to address algorithmic bias in diagnostic timing: autism as a test case,” group of Duke authors, including AI Health Director of Data Science Ben Goldstein, PhD, and Data Science Fellowship Director Matthew Engelhard, PhD, examine algorithmic approaches for models used to predict autism diagnosis. The simulation study, which is published in the journal JAMIA Open, suggests that mixture cure models show promise in improving predictive modeling in autism and potentially other conditions.

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Join the ORBIT (Observational Research Building Interdisciplinary Therapeutic Advances) Interdisciplinary Hub for a three-day Winter School in Real-World Data Analytics, January 28–30, 2026. This virtual event is open to anyone interested in real-world data analytics, regardless of affiliation with Duke University. Over three days, attendees will explore the promises and pitfalls of artificial intelligence in clinical research using real-world data, review foundational methods of causal inference and econometrics, and examine the interplay between real-world data and clinical trial design, conduct, and analysis. Speakers include AI Health Faculty Council’s Ricardo Henao and Fan Li, AI Health Faculty Affiliates Monica Agrawal and Chuan Hong, and the following faculty and experts: David Carlson, Feng Gao, Jay Lusk, Brian Mac Grory, Ryan McDevitt, Emily O’Brien, Dylan Thibault, Laine Thomas, Chengxin Yang, and Anqi Zhao.

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Inventions and emerging technologies developed at Duke were on display at the seventh annual Invented at Duke celebration, held at Duke’s Penn Pavilion on November 11. The showcase, which is hosted and sponsored by Duke’s Office of Translation and Commercialization, puts a spotlight on the university’s innovation and entrepreneurship ecosystem and offers networking opportunities for numerous featured resources – Duke AI Health among them!

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A research article published in the journal Neonatology and featuring a group of authors from Duke’s Department of Pediatrics and AI Health’s Matthew Engelhard, PhD, and Ricardo Henao, PhD, explores the use of machine learning models to improve risk predictions for retinopathy of prematurity. The article, titled “Machine Learning Risk Prediction for Treated Retinopathy of Prematurity in Infants,” appears online ahead of print.

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Following this October’s Duke Summit on AI for Health Innovation, co-hosted by Duke AI Health, Duke’s Center for Computational and Digital Health Innovation, and the Duke Clinical Research Institute, Duke AI Health Research Scientist Whitney Welsh, PhD, has compiled an impact report distilling some key information from the two-day convening, including breakdowns of conference attendees, insights from attendees, and complete lists of speakers, panelists, partners, sponsors, and discussion group representatives.

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The 2025 Medical Imaging & AI Proposal Studios selected three outstanding proposal concepts from new investigators for intensive scientific feedback and design support. During November studio sessions, investigators collaborated with some of Duke’s leading data-science experts to strengthen their proposals for upcoming high-impact funding opportunities. This effort reflects our broader commitment to accelerating innovative research at Duke through expert mentorship, interdisciplinary collaboration, and strategic proposal development.

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The Collaborative to Advance Clinical Health Equity (CACHE) at Duke Health is soliciting applications for innovative projects that leverage CACHE’s infrastructure and capabilities to identify, address, and eliminate disparities in healthcare. This RFA seeks interdisciplinary projects that utilize data science, comparative effectiveness research, predictive modeling, quality improvement, implementation science, social epidemiology, and community engagement to identify and mitigate healthcare disparities. Selected projects will receive substantial analytics, informatics, community engagement, quality improvement framework didactic training, QI engineering support, and mentorship from the CACHE team. In addition, CACHE will work with health system leaders to provide project management, informatics, and statistical support.

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Earlier this spring, Duke’s Collaborative to Advance Health Equity (CACHE) Initiative awarded 1-year grants for three proposals to improve care in the Duke Health system. Focused on addressing areas where current approaches to care and support could be improved, the three projects include identification and treatment of chronic kidney disease (CKD), transitions of care for patients with opioid use disorder (OUD), and improving screening for lung cancer, all of which pose substantial challenges among patients served by Duke Health. “We wanted to focus on work that brings together elements of care that have been shown to be effective but haven’t yet been widely integrated into clinical workflows or scaled,” notes CACHE Director Michael Pignone, MD, MPH.

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In a research article published this month in the journal BMC Medical Imaging, Duke AI Health Faculty Council Member Ricardo Henao joins first author Conor Artman to describe a new approach for using deep learning models to detect clinically significant data in echocardiograms. Unlike commonly used but data-intensive segmentation models, the authors propose a “Scaled Gumbel Softmax” deep learning model that offers improvements over existing models while requiring fewer resources.

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In an article by Mariah Drexler and Elaine Xiao that originally appeared in the Duke Chronicle, former AI Health Director Michael Pencina, PhD, and AI Health Faculty Affiliate Chuan Hong, PhD, are interviewed about their work in creating SCRIBE, a framework for evaluating AI applications that generate real-time notes during patient encounters in a hospital or other clinical setting.

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We are excited to announce the launch of the Duke AI Health Industry Studios program. Powered by AI experts in Duke’s Department of Biostatistics & Bioinformatics, we offer half- or full-day design workshops tailored to an organization’s strategy, problem-solving, and solutions development. Our Duke faculty conduct research on the frontiers of health AI methods and real-world applications and can help partners develop solutions to their toughest challenges, no matter where they are in AI implementation, from data collection and management to model development, or from measuring model performance to ongoing monitoring.

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Mark your calendars! Duke AI Health, in partnership with Duke Health and the Pratt School of Engineering, will be hosting the Health Data Science Poster Showcase on Friday, December 12, from 11:30 AM to 1:30 PM at Duke’s Fitzpatrick Center for Interdisciplinary Engineering, Medicine and Applied Sciences (FCIEMAS) Atrium, Ground Level. No registration is needed for this event, which is free and open to the public. We encourage everyone to come by and catch a glimpse of some of the innovative ideas in health data science percolating at Duke!

Join Duke AI Health for a virtual seminar, “The Protected Research Compute Cluster: Introduction for School of Medicine Researchers,” on Tuesday, November 11, 2025, from 12–1 p.m. ET. This session will introduce the Protected Research Compute Cluster (PRCC)—a new, secure high-performance computing environment that will replace PACE with expanded capacity, improved reliability, and scalable technology for sensitive data research. Presenters include Ricardo Henao, PhD, Aby Veiga, Jay Stotler, Danny Williford and John Bradley, and the session will be hosted by Shelley Rusincovitch, MMCi.

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This past October, Duke AI Health, in partnership with the Center for Computational and Digital Health Innovation at Duke University and the Duke Clinical Research Institute (DCRI) hosted the 2nd annual Duke Summit on AI for Health Innovation at the North Carolina Biotechnology Center in Research Triangle Park. The conference, which took place October 8-9, brought together experts representing a wide range of fields including healthcare, engineering, computer science, and the biomedical sciences to explore how AI can drive health innovation. Through a series of presentations, lightning talks, panel discussions, and structured “breakout” tables, participants were invited to:

• Engage in conversations about the future agenda of AI-driven health innovation;
• Network with leaders from academia, industry, and healthcare;
• Understand how to work with healthcare & learn about AI limitations and opportunities; and
• Learn about the landscape of AI development in healthcare.

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Duke AI Health Faculty Affiliate and Data Science Fellowship Director Matthew Engelhard is among the Duke faculty interviewed for a Duke Chronicle story by Lucas Lin and Ananya Pinnamaneni on the use of artificial intelligence in university learning environments. The article, which describes a pilot partnership between Duke and OpenAI to study the classroom use of ChatGPT, was later picked up by Associated Press and WRAL.

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Michael Pencina, PhD, has stepped down as vice dean for data science at Duke University School of Medicine and chief data scientist for Duke Health, effective September 30, 2025. Dr. Pencina accepted an exciting new opportunity as chief AI scientist with UnitedHealth Group. We will miss his leadership and celebrate the extraordinary foundation he has built for our school’s future in data science and artificial intelligence.

Over the past decade, Dr. Pencina has been instrumental in shaping Duke’s data science strategy for research, clinical care, and education. His leadership positioned Duke as a national leader in the responsible and rigorous application of AI in health care. Through initiatives like Duke AI Health and the Algorithm-Based Clinical Decision Support Oversight program, he has embedded governance, transparency, and trust into our approach to algorithmic innovation. As co-founder of the Coalition for Health AI (CHAI), he helped unite academic, industry, and federal stakeholders to promote trustworthy AI across the health care ecosystem.

A team at Duke University School of Medicine has received a $15 million grant from the National Institute of Mental Health to improve and expand an artificial intelligence (AI) tool that helps catch early signs of mental health problems in teenagers and adolescents. The AI model, called the Duke Predictive Model of Adolescent Mental Health (Duke-PMA), analyzes data on behavior, emotions, and brain function to identify kids at high risk for mental illness even before symptoms appear. It looks at a range of easy to measure factors, like sleep patterns and family stress, and has already shown it can predict worsening mental health up to a year in advance with 84% accuracy in kids ages 10 to 15.

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Original story by Susan Gallagher, Duke University School of Medicine

At his Techy Surgeon Substack page, Duke orthopedic surgeon and Duke Margolis Institute core faculty member Christian Péan, MD, describes his upcoming presentation at the Duke AI Summit on AI for Health Innovation, happening October 8-9, 2025, at the NC Biotechnology Center:

“I’ll be speaking about how AI-driven workflows close care gaps and improve patient experience, with a focus on agentic systems that escalate intelligently, create auditable loop closure, and support value-based care without adding burden.” 

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Earlier this month, Duke AI Health Director Michael Pencina and AI Health Communications Director Jonathan McCall were among a number of Duke faculty and staff participating in a conference on the impact of AI technologies on work, education, personal development and more. The conference, titled “The Human Edge: Our Future with Artificial Intelligence” was co-presented by Elon University and RTI and took place on RTI campus in Research Triangle Park. Elon University’s Lee Rainie also debuted findings from a recent report surveying attitudes and perceptions related to AI.
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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!

“’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 Spark, with support from Duke AI Health, invites faculty across Duke to apply for the Fall 2025 Medical Imaging and AI Proposal Studios, a unique opportunity to receive expert feedback and mentorship on grant proposals at the intersection of AI, computer vision, and medical imaging. Designed to support high-impact and competitive research, this initiative is open to clinical and non-clinical faculty from all Duke departments, with special encouragement for early-career investigators. Selected projects will participate in tailored scientific review sessions held from October to December 2025. Applications are due by 10:00 PM ET on Monday, September 8, 2025.

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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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This seminar, presented by James S. Duncan, PhD, and hosted by Maciej Mazurowski, PhD, explores advanced neuroimaging analysis in autism spectrum disorders (ASD), with a focus on identifying objective biomarkers and predicting response to behavioral therapies such as Pivotal Response Treatment (PRT). Dr. Duncan presents a range of methodologies, beginning with a Bayesian framework to detect atypical brain connectivity and moving toward machine learning approaches that use convolutional and recurrent neural networks for classification and prediction. The talk also discusses early work involving dynamic causal modeling as a complementary approach to traditional functional connectivity methods. These techniques offer promising tools for individualized diagnosis, treatment planning, and monitoring in ASD. This seminar originally aired on March 7th, 2023. This session was part of the monthly Spark: AI Health Initiative for Medical Imaging seminar series. Spark highlights cutting-edge work in medical imaging research from Duke and beyond, with a mission to develop, validate, and implement AI tools for clinical imaging. The series fostered collaboration between technical and clinical experts. VIEW

Duke AI Health Research Scientist Whitney Welsh, PhD and Managing Director Shelley Rusincovitch, MMCi, FAMIA, have published an abstract describing the use of a Delphi framework for developing effective educational strategies to address unmet needs in the domain of health AI. The abstract, titled “Using the Delphi Method to Strategize About Health AI,” was published in a supplemental issue of the Journal of Clinical and Translational Science after having been presented as a poster by Dr. Welsh at the annual meeting of the Association for Clinical and Translational Science in Washington, D.C. in April of 2025.

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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.

Duke AI Health 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. Through engaging programming and dynamic discussions, participants will explore how to leverage design thinking principles—empathy, ideation, prototyping, and testing—to ensure user-centered solutions that are practical and impactful. Mark your calendars and join us in shaping the future of AI for health! More details to follow.

The Foundations of Scientific Writing for Staff Members concept was developed and produced through the Duke AI Health Community of Practice in partnership with the Duke Clinical and Translational Science Institute (CTSI) to equip staff members to participate actively in the entire process of developing, authoring, and publishing scholarly works. Following completion of each of the two sessions of this course, we asked participants to rate their understanding of seven key concepts related to scientific writing, before and after taking the course. For both workshops, the participants came into the course with a wide range of understanding, and most felt that their understanding had improved after taking the course.  READ MORE

Thinking about attending the second annual Duke Summit on AI for Health Innovation, co-hosted by Duke AI Health and the Duke Center for Computational and Digital Health Innovation this October? Check out what attendees had to say about last year’s Summit at the link below! The Summit Impact Report for the 2024 event gathers granular feedback from over a quarter of the events nearly 100 participants. READ MORE

Earlier this June, the Duke Machine Learning Summer School 2025: Generative AI (MLSS-GenAI) concluded its five-day in-person series of classes focused on the fundamentals of generative artificial intelligence methods and applications. Sponsored by the Duke AI Health Community of Practice, the MLSS-GenAI program was led by AI Health Faculty Council Member Ricardo Henao, PhD, an associate professor in the Department of Biostatistics and Bioinformatics in the Duke University School of Medicine. Other seminar teachers included an array of Duke faculty representing biostatistics, engineering, medicine, and biology, as well as former and current Duke AI Health Faculty Affiliates.

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The Duke AI Health Community of Practice is pleased to announce our third Foundations of Scientific Writing Workshop Series, which will take place in the fall of 2025. Intended primarily for staff members interested in gaining experience with the basics of scholarly writing and publication, this mini-course consists of four virtual weekly classes that combine lecture and interactive elements, and culminates in the presentation of a final project in a poster session that will be held in December of this year.

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Duke AI Health Director Michael Pencina, PhD, recently took part in webinar hosted by Newsweek. The webinar, titled “”Health Care’s AI Playbook: Building Safe, Smart and Scalable Systems,” covered topics in AI governance and implementation for an audience of healthcare system leaders and included other participants representing industry and the nonprofit Coalition for Health AI (CHAI), of which Duke is a founding member. A summary of the event by Newsweek’s Alexis Kayser, together with a complete video recording of the hour-long webinar, is available online via Newsweek (free registration required to access). READ MORE

Dr. Connor Drake, Assistant Professor, Department of Population Health Sciences, represented a team of researchers from Duke Health, Duke Department of Population Health Sciences, and CACHE as he presented the initial findings the Eat Well trial at the May 2025 annual meeting of the Society of General Internal Medicine (SGIM) in Hollywood, Florida. Eat Well is a large-scale randomized controlled trial that was conducted at Duke to test the impact of a program of “prescription produce” on cardiometabolic outcomes. The produce prescription reduces cost barriers to healthy eating and was offered to Duke patients with elevated HbA1c levels who were at risk for food insecurity. Food insecurity is a major obstacle to improve health outcomes and equity and the findings suggest that there are key implementation factors that influence the impact of these programs in real-world care settings. LEARN MORE

This spring’s Duke Critical Care Datathon brought together more than 150 clinicians, data scientists, engineers, and students from across Duke University, Research Triangle Park and beyond for a three-day exploration of critical-care data science. Held April 25–27 at the Duke Health Center for Interprofessional Education and Care, the event combined expert-led sessions, hands-on team challenges and prototype presentations to accelerate innovations in patient monitoring, predictive modeling, and clinical decision support. This event was made possible by the generous support of Mark III Systems/NVIDIA and CloudForce. Their commitment to advancing healthcare AI provided essential computing resources, seed data science mentorship and prize funding that empowered participants to push the boundaries of critical-care analytics. We also thank our mentors, judges, and organizers—Jeremy Tan, Gloria Hyunjung Kwak, Ahram Han, Yueran Jia, Nikki Boatenhamer Freeman, Hyeon-Chul Lee and Hyung-Chul Lee—whose expertise and dedication guided each team. Datasets were graciously provided by Seoul National University Hospital, CHoRUS, MIT Critical Data and Epic Cosmos.

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Dr. Michael Cary joined an esteemed panel at the 2025 Symposium for Artificial Intelligence in Learning Health Systems (SAIL), moderated by Dr. Brendan Carr (CEO, Mount Sinai Health System), explored the transformative effects of AI on the healthcare workforce, with a special emphasis on nursing. Alongside Drs. Kenrick Cato (University of Pennsylvania) and Patricia Sengstack (Vanderbilt University), Dr. Cary discussed both the promises and concerns of AI integration, reflecting current challenges and future directions for building a workforce that is AI-ready.

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Duke AI Health and the Pratt School of Engineering are thrilled to announce the second annual Duke Summit on AI for Health Innovation, that will take place on October 8-10, 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. Through engaging programming and dynamic discussions, participants will explore how to leverage design thinking principles—empathy, ideation, prototyping, and testing—to ensure user-centered solutions that are practical and impactful. Mark your calendars and join us in shaping the future of AI for health! More details to follow.

The Duke AI Health Community of Practice is pleased to announce our third Foundations of Scientific Writing Workshop Series, which will take place in the fall of 2025. Intended primarily for operational staff members interested in gaining experience with the basics of scholarly writing and publication, this mini-course consists of four virtual weekly classes that combine lecture and interactive elements, and culminates in the presentation of a final project in a poster session that will be held in December of this year. LEARN MORE

An article that originally appeared in Duke Today describes a new AI initiative and accompanying steering committee at Duke University, helmed by Provost Alec D. Gallimore. The initiative, which encompasses the University, the School of Medicine, the School of Law, and many other departments, institutes, and centers, reflects months of development and feedback from academic leadership and faculty experts, including Duke AI Health Director Michael Pencina and Faculty Council member Ricardo Henao. Comprising four major pillars (Life with AI, Trustworthy and Responsible AI, Sustainability in AI, and Advancing Discovery with AI), the initiative is served by a new dedicated website.

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