Machine Learning in Medicine In this view of the future of medicine, patient–provider
interactions are informed and supported by massive amounts of data from interactions with …

Large language models (LLMs) have demonstrated impressive capabilities, but the bar for
clinical applications is high. Attempts to assess the clinical knowledge of models typically …

Predictive modeling with electronic health record (EHR) data is anticipated to drive
personalized medicine and improve healthcare quality. Constructing predictive statistical …

Large language models (LLMs) have demonstrated impressive capabilities in natural
language understanding and generation, but the quality bar for medical and clinical …

Machine learning is used increasingly in clinical care to improve diagnosis, treatment
selection, and health system efficiency. Because machine-learning models learn from …

Given the costs and dangers of an incorrect diagnosis, improving diagnostic accuracy has
been called the next frontier for patient safety. 1 An incorrect working diagnosis can lead to …

The study aimed to determine if computer vision techniques rooted in deep learning can use
a small set of radiographs to perform clinically relevant image classification with high fidelity …

Importance Despite the significant contribution of surgical spending to health care costs,
most surgeons are unaware of their operating room costs. Objective To examine the …

Background: Inpatient hyperglycemia is common and is linked to adverse patient outcomes.
New methods to improve glycemic control are needed. Objective: To determine whether a …

Physicians write clinical notes with abbreviations and shorthand that are difficult to decipher.
Abbreviations can be clinical jargon (writing “HIT” for “heparin induced thrombocytopenia”) …