Evidence map›Paper›PMID 40319013›Full record

Articlenpj aging2025

Assessing large language model performance related to aging in genetic conditions.

Amna A Othman, Kendall A Flaharty, Suzanna E Ledgister Hanchard, Ping Hu, Dat Duong, Rebekah L Waikel, Benjamin D Solomon

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in npj aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Disparate language and model effects on AI-based translation and recognition of genetic conditions.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  3. Article
  4. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Amna A Othman *Medical Genomics Unit, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. amna.othman@nih.gov.ORCID http://orcid.org/0000-0001-7309-7243
Kendall A Flaharty *Medical Genomics Unit, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Suzanna E Ledgister HanchardMedical Genomics Unit, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Ping HuMedical Genomics Unit, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Dat DuongMedical Genomics Unit, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Rebekah L WaikelMedical Genomics Unit, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
Benjamin D SolomonMedical Genomics Unit, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA. solomonb@mail.nih.gov.

Funding

U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) N/A (NIH intramural funding)
6 · The paper itself

Abstract

Most genetic conditions are described in pediatric populations, leaving a gap in understanding their clinical progression and management in adulthood. Motivated by other applications of large language models (LLMs), we evaluated whether Llama-2-70b-chat (70b) and GPT-3.5 (GPT) could generate plausible medical vignettes, patient-geneticist dialogues and management plans for a hypothetical child and adult patients across 282 genetic conditions (selected by prevalence and categorized based on age-related characteristics). Results showed that LLMs provided appropriate age-based responses in both child and adult outputs based on Correctness and Completeness scores graded by clinicians. Sub-analysis of metabolic conditions including those typically presents neonatally with crisis also showed age-appropriate LLM responses. However 70b and GPT obtained low Correctness and Completeness scores at producing plausible management plans (55-66% for 70b and a wider range, 50-90%, for GPT). This suggests that LLMs still have some limitations in clinical applications.

Identifiers

PMID40319013
PMCPMC12049513

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Registered trials

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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 OpenQuestion graph.