National Library of Medicine (NLM)

The NLM Mission is to support research efforts that contribute to advancements in the field of biomedical informatics and data science, with far-reaching implications for improving health and well-being. NLM fosters the advancement of cutting-edge technologies and methodologies, contributes to biomedical informatics and data science advancement, and develops strategies to improve health.

The National Library of Medicine (NLM) supports research that advances biomedical discovery, clinical care, and public health by strengthening the data, information, and knowledge ecosystems that underpin modern biomedicine. NLM’s priorities emphasize foundational biomedical informatics, trustworthy and reproducible artificial intelligence (AI), scalable and sustainable data resources, and informatic tools with real-world impact.

NLM supports research in the following areas:

Area 1: Advancing Trustworthy, Reproducible, and Rigorous Biomedical AI
• Methods to evaluate and benchmark AI for bias, reliability, security, and generalizability
• Tools and frameworks for interpretable, explainable, reproducible, and fit-for-purpose AI
• Real-world studies of AI deployment and impact
• Scalable approaches integrating complex biomedical and health data

Area 2: Biomedical Data Infrastructure at Scale
• Privacy-preserving data linkage, federation, and integration
• Semantic interoperability and metadata harmonization
• Risk- and benefit-aware data curation and evaluation frameworks
• Standards supporting scalable, interoperable data ecosystems

Area 3: Sustainable Biomedical Reference Resources and Platform Science
• Methods to enhance usability, access, and discovery
• Approaches that support reference datasets, metrics, and frameworks for quality assurance and AI benchmarking
• Approaches that support cost-effective, interoperable platform architectures and services
• Approaches for monitoring, maintenance, drift detection, and responsible sunsetting

Area 4: Biomedical Tools and Methods—Human-Centered Use and Impact
• User-centered tools for discovery, navigation, and application of authoritative knowledge
• Workflow-integrated and validated AI for research and clinical decision-making
• Evaluation of AI safety, trust, and misinformation mitigation of inaccuracies
• Secure, public-facing tools supporting evidence-based health actions

Highlighted Topics