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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 [...]

Evaluate whether large language models reason or simply regurgitate training data in clinical diagnosis.

Despite the significant potential for Clinical Decision Support Systems (CDSSs) to improve care processes and health outcomes, several barriers hinder their widespread implementation in healthcare. While numerous systematic reviews have summarized potential barriers and facilitators for CDSS implementation, a comprehensive framework to guide and evaluate the implementation of CDSSs in healthcare is lacking. This overview of reviews, aims to establish a framework-GUIDE-CDSS-aimed at guiding and evaluating implementation of CDSSs in [...]

To understand how hospitals receive and use data on patients' health-related social needs (HRSN) from external sources, and how methods of receipt relate to use.

To examine the evolving role and application of SNOMED CT (SCT) during 2020-2025, a period marked by the COVID-19 pandemic and accelerated adoption of artificial intelligence (AI) in healthcare.

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.

This study evaluated the optimization of a medication-related clinical decision support system (MRCDSS) in a 1000-bed general hospital in China by analyzing clinician responses.