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We evaluated the quality and adoption of a large language model (LLM)-based summarization tool for ongoing hospital care.

Prediction models are increasingly used in healthcare for risk stratification and personalized care. Many models are developed using machine learning, which requires tuning hyperparameters to maximize performance based on a chosen loss function metric. In healthcare, the area under the receiver operating characteristic curve (AUROC) is commonly used for this purpose, but it may not always be the most appropriate choice for every clinical application. We empirically characterize whether the [...]

Artificial intelligence (A.I.) technologies are increasingly deployed across clinical care, yet reimbursement remains a major barrier to sustainable adoption. The Centers for Medicare & Medicaid Services (CMS) currently reimburses A.I.-enabled technologies through a fragmented set of procedural codes, add-on payments, and legacy payment models, none of which were designed to support the complexity or workflow integration of clinical A.I. This article examines how existing CMS reimbursement pathways for A.I. function [...]

Ambient artificial intelligence (AI) documentation is increasingly used to draft clinical notes from patient-provider conversations, but how clinicians revise and finalize these drafts is not well understood. This qualitative content analysis study characterizes real-world edits to AI-generated drafts and identifies opportunities for improvement of AI design and the implementation process.

To assess whether large language model (LLM)-based clinical trial screening judgments vary by patient sociodemographic characteristics.

This study explores patient motivations and preferences for sharing medical data with researchers using the iAgree platform. We examine how study characteristics, including data type requested and data-sharing arrangements, influence consent decisions, and assess the role of demographic factors, privacy concerns, and perceived benefits in shaping data-sharing behavior.

The automation of medical report generation using large language models (LLMs) could significantly reduce physicians' documentation burden while enhancing healthcare efficiency. However, the misuse of generative artificial intelligence in medical reporting can lead to important safety risks for patients. We addressed 2 questions: (1) What is the quality of medical reports generated by LLMs in English and French? and (2) Can we distinguish between human-written and LLM-generated medical reports?

To evaluate if a single-subject study (S3) design, utilizing paired transcriptome samples from the same patient (eg, "sepsis" vs "recovered"), can replicate transcriptomic signatures from small case-control studies, addressing challenges in patient accrual for rare or sub-stratified diseases.

Artificial intelligence (AI) is increasingly prevalent. Patients and clinicians may use AI-based tools in many different languages.

While large language models (LLMs) have been widely used to assist clinicians and support patients, no existing work has explored dialogue systems for standard diagnostic interviews and assessments. This study aims to bridge the gap in mental healthcare accessibility by developing an LLM-powered dialogue system that replicates clinician behavior.