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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 [...]

Dr. Kevin B. Johnson delivered this address on May 16, 2026, at the Commencement Ceremony of The D. Bradley McWilliams School of Biomedical Informatics, UTHealth Houston, to the graduating class of 2026. The address uses the concept of "The Big Mo" (compounding momentum) as a frame for understanding the current inflection point in AI and medicine. Drawing on his own career arc from paper-based clinical practice at Johns Hopkins through [...]

Clarify disciplinary foundations and internal structure of biomedical informatics.

To develop the first public-facing dashboard that translates genomic sequencing data from wastewater into accessible and actionable community information concerning human pathogenic viruses, representing a shift to sequencing-based public health wastewater monitoring.

Evaluate how RAG architecture, including corpus structure, retrieval strategy, and pipeline complexity, affects LLM-based medical problem solving and knowledge retrieval in sleep medicine.

This study positions surgeon gap time, defined as the interval between consecutive surgeries performed by the same surgeon, as a surgeon-level metric of efficiency. Understanding gap time requires accounting for a surgeon's operative workload, yet no objective electronic health record (EHR)-derived measure exists. We conceptualize surgical case demand as an EHR-derived surrogate for operative workload and examine its association with surgeon gap time.

To provide a practical and methodologically grounded overview of explainable machine learning (XML) approaches in healthcare, with emphasis on their interpretation and application in clinical research and decision support. By moving beyond traditional predictive models, this primer aims to foster trust, transparency, and informed clinical decision-making, ultimately bridging the gap between data science and medical practice.

Predictive artificial intelligence (AI) promises to transform care delivery, enhance patient safety, and improve health outcomes. Realizing these benefits will require careful design, implementation, and monitoring strategies to avoid unintended consequences, including automation bias (i.e., erroneously favoring recommendations from automated systems). Automation bias is particularly concerning due to the variability of AI performance across time and populations, leading to predictions that may be variably incorrect, uncertain, or unfair.

Generative artificial intelligence (AI) chatbots built on large language models are rapidly entering mental-health care, offering human-like support without meeting evidentiary standards for safety or effectiveness.

To evaluate and compare the performance of large language models (LLMs) in identifying contributing factors (CFs) underlying patient safety incident investigations.