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A new commentary examines the shortcomings of Canada’s proposed Artificial Intelligence and Data Act and argues for a more targeted, sector-specific approach to ensure patient safety, transparency, and responsible innovation in future AI legislation. nejm.ai/4ekfI3Q

🚨 Introducing ECGFounder, a foundation model trained on over 10 million annotated ECGs, achieving expert-level performance across 150 diagnoses. Validated across diverse datasets, it generalizes well, even with single-lead inputs. NEJM AI Link: ai.nejm.org/doi/abs/10.105…

The authors of a new Perspective write about technology that alleviates the current mental health workforce shortage by focusing on the initial stages of mental health treatment for both primary care providers and mental health professionals. Learn more: nejm.ai/4k3NUSy

On the latest episode of the NEJM AI Grand Rounds podcast, Dr. Alan Karthikesalingam (Alan Karthikesalingam) shares why he believes synthetic data — and long-tail thinking — are essential for building more robust clinical AI. Listen now: nejm.ai/ep31

The authors of a new Perspective write about the landscape of medical AI in China, covering research focus areas, key players from academia and industry, and the main drivers of medical AI development. Learn more: nejm.ai/40kSswH

It was fun for me and Andrew Beam to chat with Jeremy Friese MD MBA, CEO of Humata Health, and learn about his career as an interventional radiologist & his work on AI for prior authorization. Episode out soon on NEJM AI Grand Rounds

Datasets, Benchmarks, and Protocols by D.C. de Castro (Daniel Coelho de Castro) et al.: PadChest-GR: A Bilingual Chest X-Ray Dataset for Grounded Radiology Report Generation nejm.ai/44rk1a1 #AIinMedicine

Policy Corner by Abdullah H. Ishaque, MD, PhD (Abdullah H. Ishaque), Abdi Aidid, JD, LLM, and gelareh-zadeh, MD, PhD: Lessons from the Failure of Canada’s Artificial Intelligence and Data Act nejm.ai/4ekfI3Q #AIinMedicine

Perspective by A.J. Gorelik et al.: Improving Mental Health Care Access with Technology: Addressing the Screening-to-Referral Bottleneck nejm.ai/4k3NUSy #AIinMedicine #Psychology

🧠📱 Just published in NEJM AI, we explore the promise and the perils of using #GenAI to prepare students for OSCEs. #AI can simulate patients, but it can’t replace the cognitive work of becoming a clinician. #MedEd Read more here: doi.org/10.1056/AIe250…

Could an AI system one day handle patient intake, with a doctor just reviewing the plan? On the latest episode of the NEJM AI Grand Rounds podcast, Dr. Anil Palepu envisions a future where semi-autonomous systems extend safe, expert-level care. nejm.ai/ep31

🚨 Out in NEJM AI: AI-driven simulations can help med students perform better — at scale! Our RCT shows better clinical skills, emotional readiness & less stress. 👉 Read: ai.nejm.org/doi/full/10.10… Université de Montpellier CHU de Montpellier compuute.io #EdTech #AI #OSCE #DocSimulator

Generalization does not go as expected and fine-tuning does not substitute for RAG. From NEJM AI a study by Eric Wu James Zou on fine-tuning frontier LLM's with medical data. More in the reply below

A new Perspective explores the emerging role of causal machine learning in precision oncology, highlighting its potential to improve individualized treatment decisions by estimating patient-specific treatment effects. Learn more: nejm.ai/3GOpfnl

Poignant and insightful view by medical students on the impact of AI and the role of humans in clinical care NEJM AI ai.nejm.org/doi/10.1056/AI… We need to figure out how to make students like these our future leaders in healthcare.

For some first-year medical students, training alongside an ambient AI scribe during standardized patient interviews ultimately led to professional growth and a deeper understanding of how human skills can complement AI in clinical practice. Learn more: nejm.ai/44VgDU8

Perspective by Y. Qiu et al.: The Landscape of Medical AI in China nejm.ai/40kSswH #ArtificialIntelligence #GlobalHealth

An AI-based screening tool for Parkinson’s disease (PD) that analyzes facial expressions demonstrated strong accuracy in identifying PD across diverse populations and settings, highlighting its potential for scalable, remote PD screening. Learn more: nejm.ai/4nk6PeD

Lavigne and colleagues evaluate the AI-Standardized Clinical Examination framework, which leverages text-based simulations with virtual patients and AI-driven assessment. Read the full study results: nejm.ai/3TEiuHO