BACKGROUND: Deep-learning algorithms to annotate electrocardiograms (ECGs) and classify different types of cardiac arrhythmias with the use of a single-lead ECG input data set have been developed. It remains to be determined whether these algorithms ...
Following the emergence of open public databases and connected objects, big data and artificial intelligence are developing rapidly, especially in medicine, with many opportunities ranging from complex diagnostic assistance to real-time statistical a...
OBJECTIVE: Our study compares physician judgement with an automated early warning system (EWS) for predicting clinical deterioration of hospitalised general internal medicine patients.
INTRODUCTION: To prevent errors, health care professional and safety organizations recommend using milliliters (mL) alone for oral liquid medication dosing instructions and devices. In 2018, for federal incentives under the Quality Payment Program, o...
Academic medicine : journal of the Association of American Medical Colleges
Sep 1, 2025
PROBLEM: Despite the rapidly expanding role of artificial intelligence (AI) and machine learning (ML) in health care, a significant knowledge gap remains among clinicians in their ability to evaluate and use AI and ML tools.
BACKGROUND: Large Language Models (LLMs) hold promise for clinical decision support, but their real-world performance varies. We compared three leading models (OpenAI's "o1" Large Reasoning Model (LRM), Anthropic's Claude-3.5-Sonnet, and Meta's Llama...
Efficient management of hospitalized patients requires carefully planning each stay by taking into account patients' pathologies and hospital constraints. Therefore, the ability to accurately estimate length of stays allows for better interprofession...
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