NEJM AI

From telemedicine to AI: In the latest episode of the @NEJM_AI Grand Rounds podcast, Dr. @byrondcrowe explains why recognizing the shift matters more than predicting it. Listen to the full episode: nejm.ai/ep41 #ArtificialIntelligence #AIinMedicine

Useful framework for evaluating and assigning the different combinations of human and AI agents for clinical care. Particularly useful because it recognizes that the mix will change as medicine and AI changes. NEJM AI ai.nejm.org/doi/full/10.10…

Datasets, Benchmarks, and Protocols by J.W. Hughes et al.: EchoNext-Mini: A Dataset and Baseline AI Model for Detecting Structural Heart Disease from Electrocardiograms nejm.ai/4sI5Iab #AIinMedicine

Medicine isn’t one variable — it’s a system. In the latest episode of the @NEJM_AI Grand Rounds podcast, Dr. @byrondcrowe explains why outcomes depend on everything working together. Hear more from Dr. Crowe: nejm.ai/ep41 #ArtificialIntelligence #AIinMedicine

Science and service get you in the door. Systems thinking changes everything. In the latest episode of the @NEJM_AI Grand Rounds podcast, Dr. @byrondcrowe discusses improving care at scale. Full episode: nejm.ai/ep41 #ArtificialIntelligence #AIinMedicine

Out today in @NEJM_AI: a new open source dataset and baseline model for detecting structural heart disease in ECGs! In collaboration with @PierreEliasMD @timpotsMD and the CRADLE lab ai.nejm.org/doi/full/10.10…

A new article introduces EchoNext-Mini, an open dataset of 100,000 electrocardiograms with curated structural heart disease labels and an accompanying convolutional neural network model for detecting structural heart disease from electrocardiogram data. nejm.ai/4sI5Iab

Policy Corner by Z. Ding et al.: Evaluation of Human Factors–Related Risks in AI-Enabled Medical Devices — A Practical Guide nejm.ai/4uXQTTj #ArtificialIntelligence #AIinMedicine

Original Article by Z. Ding et al.:Generating Cardiac Magnetic Resonance Images from Electrocardiograms — A Multicenter Study nejm.ai/4166ulW #ArtificialIntelligence #AIinMedicine

Catch these experts at our next event: Dr. Rebecca W. Brendel | @HMSbioethics Jonathan Ketcham | Executive Office of the President Dr. Isaac Kohane | @NEJM_AI Nikhil R. Sahni | @McKinsey Dr. Chethan Sarabu | @cornell_tech Susan M. Wolf | @UMNews 🔗 nejm.ai/4lOe1iO

In the latest episode of AI Grand Rounds, Dr. @byrondcrowe, chief medical officer of @doctronic, describes how administrative complexity can interfere with timely, effective treatment, and how AI may help address those challenges. Full episode: nejm.ai/ep41

Perspective by Yeon-Mi Hwang, PhD, Brian T. Rice, MD, and Tina Hernandez-Boussard, PhD: The Inverse Care Law in the Age of AI — Geographic Disparities in Health Care Technology Access nejm.ai/4v0RvHx #ArtificialIntelligence #AIinMedicine

Editorial by E. Ötleş (@eotles) et al.: Health Systems Govern Only the Tip of the AI Iceberg nejm.ai/4sQFxz6 #ArtificialIntelligence #AIinMedicine

A new Policy Corner proposes a structured human factors framework for AI-enabled medical devices, distinguishing technical model performance from the safety and effectiveness of human interaction w/ AI outputs in real-world clinical workflows. Learn more: nejm.ai/4uXQTTj

Across large, multicohort datasets, CardioNets achieved superior performance to ECG-only baselines and diagnostic accuracy comparable to CMR-based models, supporting its potential to expand access to advanced cardiovascular assessment. Full study results: nejm.ai/4166ulW

An Artificial Intelligence–Based Clinical Decision Support Tool to Reduce Hyponatremia after Total Joint Arthroplasty nej.md/4rAOw5S

Session 1 features our executive sponsors: Dr. Meera Kataria Atkins | @lyricclarity Ashok Chennuru | @ElevanceHealth Dr. Andrew M. Ibrahim | @viz_ai Dr. @zakkohane | @NEJM_AI Dr. David C. Rhew | @Microsoft Sign up and view the full speaker lineup: nejm.ai/4lOe1iO

A new Perspective examines the misalignment between health needs, health care resources, and AI implementation capacity, demonstrating that Hart’s inverse care law persists in the AI era. Read the full Perspective: nejm.ai/4v0RvHx

AI is increasingly used in clinical settings. However, little is known about what drives patient trust of AI during clinical encounters. Read the context and comment by Editor-in-Chief Raja-Elie Abdulnour, MD (@BageLeMage): jwat.ch/4deT5zm #MedicalAI

Health systems’ lack of engagement with consumer AI tools worsens their risk by eliminating institutional visibility and protections. Ötleş et al. describe the scope of this problem and why health systems need to reform their approach to AI governance. nejm.ai/4sQFxz6