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This study describes the utilization and experiences of artificial intelligence (AI)-generated draft responses to patient messages in pediatric ambulatory clinicians and contextualizes their experiences in relation to those of adult specialty clinicians.

To evaluate large language models (LLMs) for extracting temporal relations from pediatric rare disease clinical reports to enable automated patient timeline creation.

To evaluate effectiveness of open-source generative models in producing high-quality tabular synthetic data using a Health and Demographic Surveillance System (HDSS) dataset from rural Kenya, as a proof of concept in a low- and middle-income (LMIC) setting.

To present the Artificial Intelligence-Ready Mount Sinai (AIR·MS) platform-unified access to diverse clinical datasets from the Mount Sinai Health System (MSHS), along with computational infrastructure for AI-driven research and demonstrate its utility with 3 research projects.

To evaluate the transcription accuracy of 6 German-capable speech-to-text (STT) systems in simulated emergency medical services (EMS) environments, focusing on clinically relevant performance under noisy and multilingual field conditions.

To evaluate large language models (LLMs) for automating the assessment of clinician adherence to ESC heart failure pharmacotherapy guidelines.

This research aimed to design and evaluate a clinician-facing report generated from the SYmptom iNput Clinical (SYNC) system, a digital questionnaire for patients to report Head and Neck Cancer (HNC) symptoms and uses this to calculate a risk score. The research explored how the SYNC system could be integrated into existing hospital workflows.

To evaluate whether large language models (LLMs) can automate chart review to identify tumor necrosis factor inhibitor (TNFi) switching patterns and reasons for switching in a large real-world cohort.

Sharing clinical data for research that is both collaborative and privacy-preserving remains a challenge. Differential privacy (DP) offers a solution by introducing noise to query results. Using the PrivateSQL DP platform, this study assesses the resulting utility of differentially private data at different levels of aggregation through analyses of COVID-19 pandemic associations with new cancer diagnosis counts (NCCs).