Evidence map›Paper›PMID 41130954›Full record

ArticleNature communications2025

AgentMD: Empowering language agents for risk prediction with large-scale clinical tool learning.

Qiao Jin, Zhizheng Wang, Yifan Yang, Qingqing Zhu, Donald Wright, Thomas Huang, Nikhil Khandekar, Nicholas Wan, Xuguang Ai, W John Wilbur and 4 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. A comprehensive survey of AI agents in healthcare.Journal of biomedical informatics · 2026
    Review
  3. Article
  4. Article
  5. Medea: An omics AI agent for therapeutic discovery.bioRxiv : the preprint server for biology · 2026
    Article
  6. Article
  7. DynamiCare: A Dynamic Multi-Agent Framework for Interactive and Open-Ended Medical Decision-Making.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026
    Article
  8. State of clinical AI in 2026.BMJ digital health & AI · 2026
    Review
  9. Article
  10. SepsisCalc: Integrating Clinical Calculators into Early Sepsis Prediction via Dynamic Temporal Graph Construction.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2025
    Article
  11. Review
  12. Article
  13. Article
  14. Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2025
    Review
  15. Article
  16. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Qiao JinDivision of Intramural Research (DIR), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.ORCID http://orcid.org/0000-0002-1268-7239
Zhizheng WangDivision of Intramural Research (DIR), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.ORCID http://orcid.org/0000-0003-2584-136X
Yifan YangDivision of Intramural Research (DIR), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.ORCID http://orcid.org/0000-0003-4414-9176
Qingqing ZhuDivision of Intramural Research (DIR), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.
Donald WrightSchool of Medicine, Yale University, New Haven, CT, USA.ORCID http://orcid.org/0000-0002-2564-7754
Thomas HuangSchool of Medicine, Yale University, New Haven, CT, USA.ORCID http://orcid.org/0000-0001-9056-7016
Nikhil KhandekarDivision of Intramural Research (DIR), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.
Nicholas WanDivision of Intramural Research (DIR), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.
Xuguang AiSchool of Medicine, Yale University, New Haven, CT, USA.
W John WilburDivision of Intramural Research (DIR), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.
Zhe HeDivision of Intramural Research (DIR), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.
R Andrew TaylorSchool of Medicine, Yale University, New Haven, CT, USA.ORCID http://orcid.org/0000-0002-9082-6644
Qingyu ChenSchool of Medicine, Yale University, New Haven, CT, USA.ORCID http://orcid.org/0000-0002-6036-1516
Zhiyong LuDivision of Intramural Research (DIR), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA. zhiyong.lu@nih.gov.ORCID http://orcid.org/0000-0001-9998-916X

Funding

Addressing Factual Inaccuracy and Unfaithful Reasoning of Large Language Models in Biomedicine and HealthcareR01LM014604 · NLM · YALE UNIVERSITY · PI Qingyu Chen · 2024 to 2026
$1.1M
NLM NIH HHS R01 LM014604
6 · The paper itself

Abstract

Clinical calculators play a vital role in healthcare, but their utilization is often hindered by usability and dissemination challenges. We introduce AgentMD, a novel language agent capable of curating and applying clinical calculators across various clinical contexts. As a tool builder, AgentMD first uses PubMed to curate a diverse set of 2,164 executable clinical calculators with over 85% accuracy for quality checks and over 90% pass rate for unit tests. As a tool user, AgentMD autonomously selects and applies the relevant clinical calculators. Our evaluations show that AgentMD significantly outperforms GPT-4 for risk prediction (87.7% vs. 40.9% in accuracy). Results on 698 real-world emergency department notes confirm that AgentMD accurately computes medical risks at an individual level. Moreover, AgentMD can provide population-level insights for institutional risk management. Our study illustrates the capabilities of language agents to curate and utilize clinical calculators for both individual patient care and at-scale healthcare analytics.

Indexed as

Clinical Decision-MakingEmergency Service, HospitalHumansLanguageRisk Assessment

Identifiers

PMID41130954
PMCPMC12549800

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.