NIH-developed AI algorithm matches potential volunteers to clinical trials

A team of researchers from NIH’s National Library of Medicine and National Cancer Institute developed an AI algorithm that could successfully identify relevant clinical trials for which a person is eligible and provide a summary that clearly explains how that person meets the criteria for study enrollment. The tool can help make it easier for both clinicians and patients to find and connect with the right clinical trial opportunities.

NLM Announces New Colloquia on Biomedical Data Science and Computational Biology Research

The National Library of Medicine (NLM) is pleased to announce the launch of the NLM Colloquia on Biomedical Data Science and Computational Biology Research, a new series of regular scientific lectures from experts in the rapidly evolving fields of biomedical data science and computational biology research. The series is presented by NLM’s Division of Intramural Research (DIR), a premier hub of innovation for biomedical data science and computational biology research.

National Library of Medicine Establishes New Division of Intramural Research

The National Library of Medicine (NLM)’s Intramural Research Program (IRP) has been officially renamed and reorganized as the Division of Intramural Research (DIR) led by NLM Scientific Director Richard Scheuermann, PhD. This milestone reflects NLM’s ongoing activities based on recommendations from a Blue Ribbon Panel Review of the intramural research program, aligns with NLM’s strategic direction, and represents a positive investment in growing a center of excellence for innovation in computational biology and health informatics research.

An Extramural Program Webinar: Toward Gold Standards in Data Creation – AI Strategies to Address Data Accessibility Challenges in Biomedical Research

This webinar is a unique opportunity for data scientists, researchers, technologists, and those curious about NLM funded research to delve into collaborative strategies for creating high-quality datasets, reducing expert burden through innovative data processing techniques, and fostering community engagement and collaboration.