Why can signals that appear consistent across many studies still reflect shared bias; how do sibling comparisons help recalibrate cumulative evidence; and what AI-enabled approaches can add to large-scale evidence integration? Viktor H. Ahlqvist, PhD, from the Karolinska Institute joins JAMA and JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH, to discuss why automated drug safety surveillance during pregnancy is urgently needed and how AI and computation can strengthen or undermine causal inference. Related Content:

• AI Drug Safety in Pregnancy

How might AI amplify epidemiological insight into neurodegenerative and systemic disease? JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH, speaks with Fang Fang, MD, PhD, professor at Karolinska Institutet and head of the Integrative Epidemiology group. Drawing on Fang Fang's work in ALS, Parkinson disease, dementia, energy metabolism, immune modulation, and gut microbiome interactions, their conversation probes how AI methods might help map disease trajectories, identify prognostic markers, and ultimately support precision prevention and translational research, while preserving scientific rigor and interpretability. Related Content:

• AI and the Epidemiology of Complex Disease

What are the safety, evidence standards, and transparency needed for AI chatbots used in mental health contexts, particularly for young people. John Torous, MD, MBI, JAMA Psychiatry Author Interviews podcast host, joins JAMA+ AI Associate Editor Yulin Hswen, ScD, to discuss risks, data protections, and the clinical safeguards required to ensure responsible use. Related Content:

• AI Chatbots and Youth Mental Health

Dr Robert Wachter, chair of the Department of Medicine at UC San Francisco, speaks with JAMA+ AI Editor in Chief Roy Perlis about his new book, "A Giant Leap." Their discussion addresses multiple potential impacts of AI in medicine in terms of clinical practice but also training the next generation of clinicians. Related Content:

• Leaping Forward Into…What?—An Interview With Robert M. Wachter

In this episode, JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH, speaks with David Wu, MD, PhD, and Adam Rodman, MD, MPH, about what safe clinical use of LLMs requires. Drawing on the framework of Do No Harm, they examine failure modes, limits of accuracy-based evaluation, clinician AI interaction, and safeguards needed as medical AI moves into patient care. Related Content:

• From AI Bench to AI Bedside

In this episode of JAMA+ AI Conversations, Editor in Chief Roy Perlis and Associate Editor Yulin Hswen debate recent articles highlighted in JAMA+ AI, including work on patient messaging and suicide screening, plus a call for more critical thinking in medicine. Related Content:

• Stumbling Toward AI in the Clinic

A large language model (LLM) details the history of 2 early chatbots, ELIZA and PARRY, in conversation with JAMA+ AI Editor in Chief Roy Perlis. This podcast was recorded using OpenAI's ChatGPT in voice mode, via web interface, running on GPT-4o. Related Content:

• What Can 50-Year-Old Chatbots Teach Us About Clinical Applications of AI?

Retinal images are becoming powerful windows into human health. Cecilia Lee, MD, MS, joins JAMA and JAMA+ AI Associate Editor Yulin Hswen to explore how AI-enhanced imaging reveals early disease signals, leverages large datasets, and shifts clinical practice in ophthalmology. Related Content:

• Insights From the Eye With AI

AI chatbots have advanced rapidly, incorporating new reasoning architectures that reshape decision-making and medical education. Jonathan Chen, MD, PhD, and Ethan Goh, MD, MS, of Stanford University join JAMA and JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH, to discuss the latest generation of AI models, the importance of evaluating benefits and harms, and sycophancy in AI systems. Related Content:

• Multiple Reasoning Models and the Future of AI Chatbots
• AI Chatbots in Clinical Practice
• Large Language Model Influence on Diagnostic Reasoning

JAMA+ AI Editor in Chief Roy Perlis and Associate Editor Yulin Hswen discuss some of the most cited AI papers in 2025 across the JAMA Network. A little bit of gen AI, some prediction models, and deep learning from videos as well as an increasing awareness of the challenges of real-world deployment. Related Content:

• AI and Health—The Year in Review