ArticleJournal of atherosclerosis and thrombosis2025
ChatGPT Responses to Clinical Questions in the Japan Atherosclerosis Society Guidelines for Prevention of Atherosclerotic Cardiovascular Disease 2022.
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.
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.
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.
Who cites it
4 citing papers in PubMed.
- Evaluation of large language models in cardiovascular surgery: a comparative study of board-level clinical question answering and generation.Journal of cardiothoracic surgery · 2026Article
- Accuracy of ChatGPT and DeepSeek in answering clinical questions from the 2025 Society for Cardiovascular Angiography & Interventions/Heart Rhythm Society left atrial appendage occlusion guidelines.The Journal of international medical research · 2026Article
- Evaluation of a retrieval-augmented generation system using a Japanese Institutional Nuclear Medicine Manual and large language model-automated scoring.Radiological physics and technology · 2025Article
- Can Large Language Models Help Healthcare?Journal of atherosclerosis and thrombosis · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
What Socratic holds
Registered trials
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.