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Chatbots are increasingly used to deliver health education, patient engagement, and access to healthcare services. GARDE-Chat is an open-source platform designed to facilitate the development, deployment, and dissemination of chatbot-based digital health interventions across different domains and settings.

Despite rapid integration into clinical decision-making, clinical large language models (LLMs) face substantial translational barriers due to insufficient structural characterization and limited external validation.

Population health management programs coordinate care for over 80 million Medicaid beneficiaries but lack systematic clinical decision support for determining when to intervene and which interventions to select for patients with complex conditions. Our objective was to develop and validate a clinical decision support system integrating acuity prediction and intervention selection models for population health management programs.

Ambient digital scribe (ADS) platforms, which combine ambient speech recognition and large language models to generate clinical documentation, are currently undergoing rapid clinical adoption. Early data suggest that ADS utilization may reduce documentation burden and improve provider efficiency; however, the ethical implications of this largely unregulated technology remain relatively unexamined.

To understand the heterogeneous treatment effects of electronic alerts for acute kidney injury (AKI).

We discuss implications of potential ascertainment biases for studies examining diabetes risk following SARS-CoV-2 infection using electronic health records (EHRs). We quantitatively explore sensitivity of results to misclassification of COVID-19 status using data from the U.S.-based Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network on children (≤17 years) and young adults (18-44 years).

To develop AutoReporter, a large language model (LLM) system that automates evaluation of adherence to research reporting guidelines.

Trafficked persons experience adverse health consequences and seek help, but many go unrecognized by health-care professionals. This study explored professionals' perspectives on current approaches toward identifying and supporting trafficked persons in health-care settings, highlighting current technology roles, gaps, and future directions.

To develop and evaluate a knowledge graph-augmented large language model (LLM) framework that synthesizes epidemiological evidence to infer life-course exposure-outcome pathways, using gestational diabetes mellitus (GDM) and dementia as a case study.