Evidence map›Paper›PMID 40785818›Full record

ArticleCirculation reports2025

Performance Evaluation of Large Language Models With Retrieval-Augmented Generation in Cardiology Specialist Examinations in Japan.

Hiromasa Hayama, Tu Hao Tran, Jin Kirigaya, Yosuke Katayama, Tomoko Negishi, Koya Ozawa, Kazuaki Negishi

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In one paragraph

Article in Circulation reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Hiromasa HayamaSchool of Clinical Medicine, University of New South Wales Sydney, NSW Australia.
Tu Hao TranImaging and Phenotyping Laboratory, Charles Perkins Centre, University of Sydney Sydney, NSW Australia.
Jin KirigayaSchool of Clinical Medicine, University of New South Wales Sydney, NSW Australia.
Yosuke KatayamaSchool of Clinical Medicine, University of New South Wales Sydney, NSW Australia.
Tomoko NegishiSchool of Clinical Medicine, University of New South Wales Sydney, NSW Australia.
Koya OzawaSchool of Clinical Medicine, University of New South Wales Sydney, NSW Australia.
Kazuaki NegishiSchool of Clinical Medicine, University of New South Wales Sydney, NSW Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Large language models (LLMs) have shown potential in medical education, but their application to cardiology specialist examinations remains underexplored. We compared the performances of a retrieval-augmented generation LLM (RAG-LLM) 'CardioCanon' against general-purpose LLMs. Methods and Results: A total of 96 publicly available text-based open-source multiple-choice questions from the Japanese Cardiology Specialist Examination (1997-2022) were used. CardioCanon showed similar option-level accuracy to ChatGPT-4o and Gemini 2.0 Flash (81.0%, 76.0%, and 77.2%, respectively), but higher case-based accuracy than ChatGPT (57.3% vs. 29.2%, P<0.001). Conclusions: RAG techniques can enhance AI-assisted examination performance by improving case-level reasoning and decision-making.

Indexed as

Cardiology examinationLarge language modelMedical educationRetrieval augmented generation

Identifiers

PMID40785818
PMCPMC12331345

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.