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When patients are admitted to the hospital, clinical care teams must enter an expected discharge date (CT-EDD) into the electronic health record (EHR). This is often challenging for neonates admitted to the neonatal intensive care unit (NICU) due to extended lengths of stay. Pieces, a generative artificial intelligence (AI) program, uses EHR data and machine learning (ML) algorithms to predicted discharge dates (AI-PDD) for patients anticipated to discharge home in [...]

Large language models (LLMs) are increasingly used as automated evaluators of medical text ("LLM-as-judge"). We tested whether LLM-evaluator performance validated on one medical task against a human reference standard remains stable on a structurally different task.

Systematic evaluation of variability in large language model (LLM)-generated outputs is critical for assessing their reliability. We developed a general statistical framework and Python package (llmReproScore) for quantifying the repeatability and reproducibility of LLM outputs.

The clinical practice guidelines (CPGs) published by the European Society for Vascular Surgery (ESVS) span approximately 1100 pages whose simultaneous cross-referencing is impractical at the point of care. Unguarded large language models (LLMs) carry hallucination risk, motivating a guideline-locked design.

To examine the associations of medical discrimination with hypertension (HTN) outcomes.

Cardiac surgeries are associated with a range of serious postoperative complications, and current predictive models often rely on static summaries of preoperative and intraoperative data compiled after the case. Such development limits their utility in real-time clinical settings. To address this challenge, we developed TE-Transformer, an end-to-end deep learning framework that continuously ingests heterogeneous electronic health record (EHR) data-including preoperative variables, high-frequency vital signs, medication administration records, and brief surgical [...]

To demonstrate a replicable in-house framework for evaluating ambient digital scribe (ADS) systems and, applying it, to assess 3 commercial systems and examine alignment between clinician Likert ratings and a manual transcript-level error audit.

To evaluate whether retrieval-augmented generation (RAG) can serve as an efficient alternative to long-context prompting for clinical reasoning over electronic health records (EHRs).

Artificial intelligence (AI) is increasingly being used in healthcare settings, yet evidence of its real-world value remains inconsistent. Current evaluation paradigms often emphasize methodological rigor and technical validity over measurable improvements in patient outcomes or system performance.