Evidence map›Paper›PMID 39477517›Full record

ArticleJournal of atherosclerosis and thrombosis2025

ChatGPT Responses to Clinical Questions in the Japan Atherosclerosis Society Guidelines for Prevention of Atherosclerotic Cardiovascular Disease 2022.

Takashi Hisamatsu, Mari Fukuda, Minako Kinuta, Hideyuki Kanda

Abstract read
In one paragraph

Article in Journal of atherosclerosis and thrombosis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Can Large Language Models Help Healthcare?Journal of atherosclerosis and thrombosis · 2025
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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

4 authors.

Takashi HisamatsuDepartment of Public Health, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences.
Mari FukudaDepartment of Public Health, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences.
Minako KinutaDepartment of Public Health, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences.
Hideyuki KandaDepartment of Public Health, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsArtificial intelligence is increasingly used in the medical field. We assessed the accuracy and reproducibility of responses by ChatGPT to clinical questions (CQs) in the Japan Atherosclerosis Society Guidelines for Prevention Atherosclerotic Cardiovascular Diseases 2022 (JAS Guidelines 2022).

methodsIn June 2024, we assessed responses by ChatGPT (version 3.5) to CQs, including background questions (BQs) and foreground questions (FQs). Accuracy was assessed independently by three researchers using six-point Likert scales ranging from 1 ("completely incorrect") to 6 ("completely correct") by evaluating responses to CQs in Japanese or translated into English. For reproducibility assessment, responses to each CQ asked five times separately in a new chat were scored using six-point Likert scales, and Fleiss kappa coefficients were calculated.

resultsThe median (25th-75th percentile) score for ChatGPT's responses to BQs and FQs was 4 (3-5) and 5 (5-6) for Japanese CQs and 5 (3-6) and 6 (5-6) for English CQs, respectively. Response scores were higher for FQs than those for BQs (P values <0.001 for Japanese and English). Similar response accuracy levels were observed between Japanese and English CQs (P value 0.139 for BQs and 0.586 for FQs). Kappa coefficients for reproducibility were 0.76 for BQs and 0.90 for FQs.

conclusionsChatGPT showed high accuracy and reproducibility in responding to JAS Guidelines 2022 CQs, especially FQs. While ChatGPT primarily reflects existing guidelines, its strength could lie in rapidly organizing and presenting relevant information, thus supporting instant and more efficient guideline interpretation and aiding in medical decision-making.

Indexed as

Artificial IntelligenceAtherosclerosisCardiovascular DiseasesPractice Guidelines as TopicGenerative Artificial IntelligenceHumansJapanReproducibility of ResultsSocieties, MedicalSurveys and QuestionnairesAccuracyAutonomic intelligenceChatGPTGuidelinesReproducibility

Identifiers

PMID39477517
PMCPMC12055503

What Socratic holds

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LicenceCC BY-NC-SA
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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.