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To evaluate the clinical applications and translation readiness of model-based synthetic tabular data in healthcare, and identify gaps in governance reporting that may hinder translation.

People with type 1 diabetes mellitus (T1DM) show glucose variability driven by insulin dosing, meals, activity, and circadian rhythms. Many deep learning approaches treat glucose forecasting and hypoglycemia detection as separate tasks and provide limited transparency.

To characterize emergency department (ED) clinician engagement with electronic health record (EHR)-based social drivers of health (SDOH) data; test whether engagement differs in encounters with opioid use disorder (OUD); and, among OUD encounters, assess whether engagement is associated with medications for OUD (MOUD).

Systematic reviews remain labor-intensive, particularly when extracting methodological details from full texts. Using mediation analysis as a case study, we evaluated whether large language models (LLMs) can match human-expert-level full-text methodological review on key causal assumptions (eg, no unmeasured confounding, temporal ordering) and best practices (eg, sensitivity analyses, interaction assessments, covariate adjustment) for psychiatry and psychology studies.

This address was delivered by Eric Horvitz, MD, PhD, at the 2026 graduation ceremony of Columbia University School of Nursing on May 19, 2026, where he received the Second Century Award for Excellence in Health Care. The address considers the responsibilities of clinicians in shaping the future of artificial intelligence in medicine. It frames health care as an "open world," where information is incomplete, time is limited, and decisions are [...]

To develop privacy-enhancing statistical methods for estimating disease risk parameters across multiple electronic health record (EHR) sites with heterogeneous selection mechanisms, avoiding individual-level data sharing. We illustrate their utility via a cross-biobank analysis of smoking and 97 cancer subtypes using NIH All of Us (AOU) and Michigan Genomics Initiative (MGI) data sites.

Large language models (LLMs) have shown promise in clinical applications, yet prior studies mainly evaluated their standalone performance on benchmarks or examinations. This study assesses how LLMs support clinicians across specialties, disease contexts, experience levels and decision-making stages.

To assess prevalence, characteristics, and institutional predictors of clinical informatics (CI) education and student organizations in US allopathic (MD) and osteopathic (DO) medical schools.

To enhance the accuracy, interpretability, and robustness of large language models (LLMs) in medical question answering (MedQA).

To conduct a scoping review of bias assessment in studies applying large language models (LLMs) to health data and to synthesize their prevailing conceptualization of bias.