Cristiana Baloescu, an Assistant Professor at Yale University specializing in emergency medicine and machine learning for ultrasound, joins Yulin Hswen to discuss cutting-edge research on AI-guided lung ultrasounds. They delve into how AI can enhance image acquisition for non-expert clinicians, improving accuracy in diagnosing dyspnea. The conversation also highlights real-world challenges in implementing this technology, particularly in resource-limited settings, and the potential for AI to transform emergency care.

Sanjat Kanjilal, an assistant professor at Harvard Medical School, explores how machine learning is reshaping the diagnosis and treatment of infectious diseases. He discusses a groundbreaking study on urinary tract infections, revealing the effectiveness of traditional antibiotics despite outdated guidelines. Kanjilal emphasizes the importance of understanding antimicrobial resistance and the challenges of biases in AI within healthcare. He advocates for better data infrastructure to harness AI's full potential in enhancing patient care and medical decision-making.

Cornelius James, MD, a Clinical Assistant Professor at the University of Michigan, shares insights on the surprising adoption of digital health technologies by older adults. He challenges societal biases about seniors' tech engagement and discusses how patient portals enhance access to healthcare. The conversation explores the digital health divide faced by rural and low-income groups, the importance of in-person care, and how healthcare providers can support older patients in navigating technology. AI's role in improving healthcare experience is also highlighted.

How do patients feel about the quality of AI-generated responses to their messages to clinicians? Author Eleni Linos, MD, DrPH, of Stanford joins JAMA+ AI Editor in Chief Roy H. Perlis, MD, MSc, to discuss her recent study in JAMA Network Open that characterized satisfaction with these messages. Related Content: Study Finds People Prefer AI Over Clinician Responses to Questions in the Electronic Medical Record Perspectives on Artificial Intelligence–Generated Responses to Patient Messages

Rita Rubin, the lead senior staff writer at JAMA, dives into the transformative impact of weight-loss drugs like GLP-1 on bariatric surgery. She discusses the decline in surgical bookings as these medications gain traction. The conversation also covers safety concerns surrounding compounded GLP-1 drugs and the shifting perspectives on cancer classifications. Rubin raises thought-provoking questions about renaming certain cancers and how these changes can influence patient treatment choices, ultimately advocating for better patient awareness.

Eden English, Senior Medical Director of Informatics at UCHealth and a respected author, shares her insights on the use of AI to ease clinician burnout. She discusses how tools like PamChat streamline communication by drafting replies to patient messages. The conversation also delves into refining AI prompts to improve response accuracy and the critical need for clinicians to oversee AI-generated messages. English highlights the challenge of misinformation, particularly around vaccines, and the ethical considerations of integrating AI in patient care.

Maria Villalobos-Quesada is a postdoctoral researcher at the National eHealth Living Lab and the University of Barcelona, focusing on the bioethics of health tech. In this engaging discussion, she addresses the rapid adoption of AI in healthcare and the ethical frameworks emerging to protect vulnerable populations. Villalobos-Quesada highlights the risks posed by AI, such as a scandal in the Netherlands, and emphasizes the necessity for ongoing evaluation and transparency in AI systems to prevent bias and ensure effectiveness.

Emily Haroz, an Associate Professor at Johns Hopkins Bloomberg School of Public Health, specializes in mental health and suicide prevention in Indigenous communities. She discusses alarming suicide rates among American Indian and Alaska Native populations and highlights an AI-based screening tool designed specifically for these communities. Haroz delves into how AI can revolutionize mental health interventions, emphasizing cultural sensitivity and the need for community involvement. She also addresses ethical concerns and the importance of collaboration between AI and traditional practices.

How can hospitals use early warning score tools to risk stratify patients without adding to alarm fatigue? Dana Edelson, MD, MS, of the University of Chicago joins JAMA+ AI Editor in Chief Roy H. Perlis, MD, MSc, to discuss a recent study published in JAMA Network Open that she coauthored, comparing 6 early warning scores designed to recognize clinical deterioration in hospitalized patients. Related Content: Researchers Compared Hospital Early Warning Scores for Clinical Deterioration—Here’s What They Learned Early Warning Scores With and Without Artificial Intelligence

Chatbots may have a role in enhancing clinical care, but the best way to apply them remains a work in progress. Jonathen Chen, MD, PhD, and Ethan Goh, MD, MS, of Stanford, join JAMA and JAMA+ AI Associate Editor Yulin Hswen, ScD, MPH, to discuss their randomized clinical trial published in JAMA Network Open investigating the use of chatbots in clinical practice. Related Content: An AI Chatbot Outperformed Physicians and Physicians Plus AI in a Trial—What Does That Mean? Large Language Model Influence on Diagnostic Reasoning