Evidence map›Paper›PMID 40063704›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2025

How the National Library of Medicine should evolve in an era of artificial intelligence.

Leslie Andrew Lenert

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Leslie Andrew LenertBiomedical Informatics Center, Medical University of South Carolina, Charleston, SC 29405, United States.ORCID 0000-0002-9680-5094

Funding

Medical University of South CarolinaSmart State Endowed Chair for Healthcare Quality
6 · The paper itself

Abstract

objectivesThis article describes the challenges faced by the National Library of Medicine with the rise of artificial intelligence (AI) and access to human knowledge through large language models (LLMs). BACKGROUND AND SIGNIFICANCE: The rise of AI as a tool for the acceleration and falsification of science is impacting every aspect of the transformation of data to information, knowledge, and wisdom through the scientific processes. APPROACH: This perspective discusses the philosophical foundations, threats, and opportunities of the AI revolution with a proposal for restructuring the mission of the National Library of Medicine (NLM), part of the National Institutes of Health, with a central role as the guardian of the integrity of scientific knowledge in an era of AI-driven science.

resultsThe NLM can rise to new challenges posed by AI by working from its foundations in theories of Information Science and embracing new roles. Three paths for the NLM are proposed: (1) Become an Authentication Authority For Data, Information, and Knowledge through Systems of Scientific Provenance; (2) Become An Observatory of the State of Human Health Science supporting living systematic reviews; and (3) Become A hub for Culturally Appropriate Bespoke Translation, Transformation, and Summarization for different users (patients, the public, as well as scientists and clinicians) using AI technologies. DISCUSSION: Adapting the NLM to the challenges of the Internet revolution by developing worldwide-web-accessible resources allowed the NLM to rise to new heights. Bold moves are needed to adapt the Library to the AI revolution but offer similar prospects of more significant impacts on the advancement of science and human health.

Indexed as

Artificial IntelligenceNational Library of Medicine (U.S.)HumansUnited Statesartificial intelligencedatainformationknowledgeNational Library of Medicine

Identifiers

PMID40063704
PMCPMC12012362

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.