NEJM AI

On AI Grand Rounds, Dr. Michal Nedoszytko of Abridge explains how AI can help physicians gather better information before an encounter, improve the quality of conversations with patients, and prototype solutions to problems that clinicians experience every day. Listen now: https://nejm.ai/ep47

Editorial by Adam A. Markovitz, MD, PhD (@adammarkovitz.bsky.social), and Alexander M. Chaitoff, MD, MPH: Comprehensive or Wasteful? Clinical AI and the Up-front Workup https://nejm.ai/4zyYg59 #ArtificialIntelligence #AIinMedicine

Perspective by P. Jarrett et al.: No Mastery without the Machine — Medical Education in the Age of Clinical AI https://nejm.ai/4ybNSiw #MedEd #AIinMedicine

🧪 A new study found care recommended by LLMs costs 3.2x more than plans from treating physicians ($314 vs. $97 per visit at Medicare rates). Across 200 real clinical encounters, AI ordered 5.6x more specialist referrals and nearly 2x as many new medications. #MedSky

A new study develops and externally validates an AI-enhanced digital auscultation system for detecting left ventricular dysfunction using prospective multicenter data. Learn more: https://nejm.ai/3W3zag4

A new randomized controlled trial evaluated whether AI-generated, layperson-friendly summaries of medical findings provided before outpatient consultations improve physician–patient communication. Learn more: https://nejm.ai/3W3z9Zy

A new study examines whether the clinical care recommended by contemporary LLMs carries a different cost than the care recommended by treating physicians, and whether that difference is influenced by how the models are prompted. Learn more: https://nejm.ai/4i18zYw

On NEJM AI Grand Rounds, @xiaoliu.bsky.social is excited by the possibility of combining medical knowledge to uncover new biomarkers, disease phenotypes, and treatment-response patterns that challenge today’s categories. Listen to the full episode: https://nejm.ai/ep46

A new editorial discusses the need to assess the use of AI technology to understand how it impacts what matters to the patient and to society. Full editorial: https://nejm.ai/4zyYg59

A new Perspective suggests that medical education must adapt as AI shifts clinical expertise from unaided cognitive performance toward the ability to evaluate, supervise, and appropriately defer to increasingly capable AI systems. Learn more: https://nejm.ai/4ybNSiw

On NEJM AI Grand Rounds, @xiaoliu.bsky.social imagines clinicians becoming “companions of experience” — helping patients through deterioration, uncertainty, treatment failure, recovery, and life-changing diagnoses. Hear more from Dr. Liu: https://nejm.ai/ep46

Original Article by B. Lahrmann et al.: Closing the Automation Gap in HPV-Based Cervical Cancer Screening: Independent External Validation of an AI Model for Dual-Stain Triage https://nejm.ai/4xRsEXe #ArtificialIntelligence #AIinMedicine

Editorial by @roxanadaneshjou.bsky.social: Where Are the Prepared Minds? The Impact of AI on Scientific Thinking https://nejm.ai/3TKfJbp #ArtificialIntelligence #AIinMedicine

On NEJM AI Grand Rounds, @xiaoliu.bsky.social discusses how she moved from academic AI and clinical ophthalmology into product development because she wanted firsthand experience of the other side of the evidence pipeline. Full episode: https://nejm.ai/ep46

Case Study by V.J. Nolan et al.: Language Models for Value-Informed Proxy Decision Support https://nejm.ai/3UfoOc9 #AIinMedicine #EndOfLifeCare

A new study explores the use of AI to automate p16/Ki-67 dual-stain cytology evaluation, a key step in triaging HPV-positive patients during cervical cancer screening. Learn more: https://nejm.ai/4xRsEXe

No smoke and mirrors. In the latest episode of the NEJM AI Grand Rounds podcast, @xiaoliu.bsky.social argues that medical AI reporting standards should make methods visible rather than dictate them. Hear more from Dr. Liu: https://nejm.ai/ep46

A new editorial by @roxanadaneshjou.bsky.social argues that scientific trainees should first develop foundational skills before relying on AI, which should serve as a complementary tool, not a replacement for reasoning. Read the full editorial: https://nejm.ai/3TKfJbp

Perspective by I. Nenadic et al.: From Technical Performance to Clinical Readiness: A Phase-Based Framework for Evidence Standards in Clinical Artificial Intelligence https://nejm.ai/4yIi4CK #AIinMedicine

Policy Corner by A. Wong et al.: How Repeal of the NTAP Alternative Pathway will Impact U.S. Clinical AI Innovation https://nejm.ai/4r7fjbd #ArtificialIntelligence #AIinMedicine

Original Article by L. Poursoltan et al.: Physician Edits to AI-Drafted Patient Messages and Their Impact on Clinical Workload https://nejm.ai/4c7M3Lv #ArtificialIntelligence #AIinMedicine

Perspective by Susan Michie, DPhil (@susanmichie.bsky.social), Robert West, PhD (@robertjwest.bsky.social), and Janna Hastings, PhD (@jannahastings.bsky.social): Building an AI-Ready Evidence Base for Behavior Change https://nejm.ai/4wJFmX8 #ArtificialIntelligence #AIinMedicine

Case Study by A.M. Kerr et al.: Parental Perspectives on Using Large Language Model–Powered Chatbots in Rare Disease Care https://nejm.ai/4zChqYG #ArtificialIntelligence #AIinMedicine

On NEJM AI Grand Rounds, @xiaoliu.bsky.social says fewer than 5% of papers in their review met that bar, and the qualifying models performed at best equivalently to radiologists. The signal matters. So does separating it from the noise. Listen to the full episode: https://nejm.ai/ep46

In a new study on value-informed proxy decision support, Nolan and colleagues examine whether LLMs can improve alignment with patient preferences, extract values from clinical notes, and maintain performance across different reading levels. Full results: https://nejm.ai/3UfoOc9

Perspective by B. Sheng et al.: A Classification of Safety Risks in Medical AI https://nejm.ai/4wo9oQ4 #ArtificialIntelligence #AIinMedicine

A new Perspective proposes a five-phase framework for evaluating the evidentiary maturity of clinical AI to help distinguish technical performance from clinical readiness and align claims with evidence needed to support safe, effective use. Learn more: https://nejm.ai/4yIi4CK

A new Policy Corner analyzes how CMS’s repeal of the NTAP alternative pathway may exacerbate an existing imbalance in the U.S. market by stifling competition and driving health systems toward inferior but built-in EHR vendor–developed AI. Learn more: https://nejm.ai/4r7fjbd

The promise of medical AI is not simply better performance — it is better care grounded in evidence that can be trusted. On AI Grand Rounds, @xiaoliu.bsky.social discusses what rigorous evaluation looks like as technologies move from publications into practice. Listen now: https://nejm.ai/ep46

Perspective by Lars Masanneck, MD, MSc, and Sibylle C. Mellinghoff, MD: Agentic AI Teammates in Medical Research — From Tools to Collaborators — and the Accelerating Digital Divide https://nejm.ai/4xIiMiR #ArtificialIntelligence #AIinMedicine

Drugs. Devices. And a third modality: AI-based interventions. On NEJM AI Grand Rounds, Dr. Suchi Saria envisions software protocols that identify precisely when to act, what to do, and which patient needs it — while making delivery easier at scale. Hear more from Dr. Saria: https://nejm.ai/ep45

A new study examines parents’ perceptions of a secure LLM chatbot for pediatric cancer and vascular anomalies, highlighting opportunities for caregiver support alongside challenges related to trust, overreliance, emotional readiness, and implementation. Learn more: https://nejm.ai/4zChqYG

Perspective by Alfredo Madrid-García, PhD, Beatriz Merino-Barbancho, PhD, and Miguel Rujas, MSc: When the Chatbot Leaks: Securing Patient-Facing Medical AI in the Age of Dual-Use Large Language Models https://nejm.ai/45py8fS #ArtificialIntelligence #AIinMedicine

A new study analyzing the impact of clinicians modifying AI-generated drafts within an electronic health record showed that complex edits require the most additional time while common administrative edits contribute the greatest workload. See how: https://nejm.ai/4c7M3Lv

Documentation is not the holy grail. Better care is. On NEJM AI Grand Rounds, Dr. Suchi Saria argues that clinical AI must move into the center of the encounter, where rigorous tools can help clinicians recognize risk and act sooner. Listen to the full episode: https://nejm.ai/ep45

The authors of a new Perspective argue that realizing the potential of AI to improve behavior change interventions will require a fundamental transformation of behavioral science. Read the full Perspective: https://nejm.ai/4wJFmX8

A new study evaluates a value-informed LLM framework designed to support surrogate decision-making for patients who lose decisional capacity by comparing LLM-generated treatment recommendations with choices made by patients and their proxies. Learn more: https://nejm.ai/3UfoOc9

On NEJM AI Grand Rounds, Dr. Suchi Saria explains why health systems need evidence that AI can improve outcomes, reduce utilization, and support a sustainable path to implementation. Listen to the full episode: https://nejm.ai/ep45

Current medical AI frameworks do not sufficiently address patient safety because harms are often delayed, difficult to attribute, and irreversible. A new Perspective proposes a four-tier classification of medical AI safety. Learn more: https://nejm.ai/4wo9oQ4

A new Perspective examines how agentic AI may transform biomedical research by shifting bottlenecks from analysis itself to the data infrastructure, governance, and institutional capabilities required to deploy AI effectively. Learn more: https://nejm.ai/4xIiMiR

What changes when an abstract research problem becomes a family tragedy? On NEJM AI Grand Rounds, Dr. Suchi Saria explains how losing her nephew to sepsis led her to build Bayesian and pursue earlier, actionable recognition at the point of care. Listen to the full episode: https://nejm.ai/ep45

Sign up for our next event exploring how AI is reshaping guideline development and clinical decision-making. Leading experts will discuss how to harness the strengths of LLMs while preserving clinician autonomy, minimizing bias, and ensuring evidence continues to guide care. https://nejm.ai/4zKA5S4

A new Perspective describes an anonymized security assessment of a publicly deployed patient-facing retrieval-augmented generation chatbot and argues that patient-facing health AI systems must be evaluated as secure software deployments. Learn more: https://nejm.ai/45py8fS

Editorial by Todd Hollon, MD: Benchmarking the Brain’s Blood Vessels — Why We Need Specialized Models in Medical AI https://nejm.ai/44Nz5hT #ArtificialIntelligence #AIinMedicine

Original Article by T.A. Nahass et al.: Implementation of an AI-Triggered Rapid Response — Association with Mortality https://nejm.ai/3TsbwbS #ArtificialIntelligence #AIinMedicine

What makes medicine such a compelling test for AI? The data are messy, noisy, and biased. On NEJM AI Grand Rounds, Dr. Suchi Saria argues that this complexity is exactly where well-designed machine learning can create meaningful clinical value. Hear more from Dr. Saria: https://nejm.ai/ep45

Perspective @ai.nejm.org from @susanmichie.bsky.social & colleagues-who have devoted careers to substantial structural challenges around study of behav health-offers impt perspective on what needs to change to accelerate meaningful learning in this vitally important area. ai.nejm.org/doi/full/10....

Datasets, Benchmarks, and Protocols article by K. Yang et al.: The TopCoW Challenge — Topology-Aware Circle of Willis Segmentation for CT and MR Angiography https://nejm.ai/4fnSMTs #ArtificialIntelligence #AIinMedicine

Case Study by C.R. Cleland et al.: Will Artificial Intelligence Augment Global Health Inequalities? https://nejm.ai/4aPRgqG #ArtificialIntelligence #AIinMedicine

Editorial by A.A. Agarwal et al.: After Clearance — Continuous Monitoring as the Foundation of Clinical AI Oversight https://nejm.ai/4wffVgQ #ArtificialIntelligence #AIinMedicine