ArticleDigital health
ChatGPT response consistency to the 2025 ESC/EACTS guidelines for the management of valvular heart disease: A test-retest study using binary and multiple-choice questions.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background/Objectives: This study aimed to evaluate the response variability and temporal instability of responses generated by the artificial intelligence-based model ChatGPT-5.2 to structured clinical questions derived from the 2025 ESC/EACTS Guidelines for the Management of Valvular Heart Disease (GMVHD). Methods: This prospective observational study employed a test-retest design. A structured set of 100 guideline-based questions-comprising 60 binary (true/false) and 40 multiple-choice items-was developed by two cardiologists (Ç.M. and Z.U.) in accordance with the 2025 ESC/EACTS GMVHD. The question set was administered to ChatGPT-5.2 on two separate occasions with a 14-day interval. The model was instructed to provide answers only, without any explanatory commentary. ChatGPT-generated responses were independently evaluated and coded as correct or incorrect by two cardiologists (Y.K. and Ç.M.). Numerical changes in responses were assessed using McNemar's test, while test-retest reliability was evaluated using Cohen's kappa coefficient. Results: For binary questions, ChatGPT demonstrated an accuracy of 96.7% in both assessments. Accuracy for multiple-choice questions increased from 75.0% at baseline to 87.5% at the second assessment. When all questions were analyzed together, overall accuracy improved from 88.0% to 93.0%. A numerical increase in accuracy was observed between T1 and T2, without a statistically significant temporal difference. (p > 0.05). Cohen's kappa analysis indicated moderate agreement for binary questions and low agreement for multiple-choice questions. Conclusion: ChatGPT-5.2 demonstrated numerical improvement without statistically significant temporal difference and short-term performance change when answering guideline-based clinical questions on valvular heart disease. However, the relatively high initial error rate in multiple-choice questions represents a limitation for clinical reliability. At present, AI systems may be considered supportive tools for guideline-based information retrieval and clinical education.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.