Jassim, A.M., Shaker, A.A., Abdulgalil, G.A.K., Ali, A.W., Taiseer, A.F.N., S, A.R., Ali, A.K., & Taha, A.H.M. (2026). Evaluating ChatGPT's adherence to evidence-based heart failure guidelines: a comparative analysis using the 2023 ESC and 2022 ACC/AHA/HFSA recommendations.. Acta cardiologica. https://doi.org/10.1080/00015385.2026.2668803
Jassim AM, Shaker AA, Abdulgalil GAK, Ali AW, Taiseer AFN, S AR, et al. Evaluating ChatGPT's adherence to evidence-based heart failure guidelines: a comparative analysis using the 2023 ESC and 2022 ACC/AHA/HFSA recommendations.. Acta cardiologica. 2026; doi: 10.1080/00015385.2026.2668803
Jassim AM, Shaker AA, Abdulgalil GAK, et al. Evaluating ChatGPT's adherence to evidence-based heart failure guidelines: a comparative analysis using the 2023 ESC and 2022 ACC/AHA/HFSA recommendations.[J]. Acta cardiologica. 2026. DOI: 10.1080/00015385.2026.2668803.
@article{jassim2026,
author = {Alnuwaysir Mohammed Jassim and Aljama Abdullah Shaker and Galib Abdulkarim Kassim Abdulgalil and Aldawood Wafa Ali and AlRatrout Farah Nedal Taiseer and AlSulaiman Reem S and AlNasser Kawthar Ali and Al-Hariri Mohammed Taha},
title = {Evaluating ChatGPT's adherence to evidence-based heart failure guidelines: a comparative analysis using the 2023 ESC and 2022 ACC/AHA/HFSA recommendations.},
journal = {Acta cardiologica},
year = {2026},
doi = {10.1080/00015385.2026.2668803},
note = {PMID: 42090596},
}
TY - JOUR AU - Alnuwaysir Mohammed Jassim AU - Aljama Abdullah Shaker AU - Galib Abdulkarim Kassim Abdulgalil AU - Aldawood Wafa Ali AU - AlRatrout Farah Nedal Taiseer AU - AlSulaiman Reem S AU - AlNasser Kawthar Ali AU - Al-Hariri Mohammed Taha TI - Evaluating ChatGPT's adherence to evidence-based heart failure guidelines: a comparative analysis using the 2023 ESC and 2022 ACC/AHA/HFSA recommendations. T2 - Acta cardiologica PY - 2026 DO - 10.1080/00015385.2026.2668803 AN - PMID:42090596 ER -
BACKGROUND: Heart failure (HF) remains a major cause of morbidity and mortality worldwide. Large language models (LLMs) such as ChatGPT are emerging as potential clinical decision support tools, but their adherence to specialty guidelines is not well characterised. OBJECTIVES: To evaluate the accuracy and guideline concordance of ChatGPT-5 in managing real-world HF scenarios compared with the 2023 European Society of Cardiology (ESC) and 2022 American College of Cardiology (ACC)/American Heart Association (AHA)/Heart Failure Society of America (HFSA) recommendations. METHODS: Thirty-eight anonymised HF clinical vignettes spanning reduced, mildly reduced, and preserved ejection fraction phenotypes and varied New York Heart Association (NYHA) classes were presented to ChatGPT-5. Two board-certified cardiologists independently graded each response for concordance with guideline recommendations using a 4-point scale (3 = fully concordant, 2 = partially concordant, 1 = discordant, 0 = unsafe/harmful). Discrepancies were adjudicated by a third reviewer. Descriptive statistics summarised performance and inter-rater agreement. RESULTS: Of the 38 responses, 20 (53%) were fully concordant, 4 (11%) partially concordant, 8 (21%) discordant, and 6 (16%) unsafe/harmful. Most inaccuracies involved vague drug titration guidance, incomplete device therapy recommendations, or omission of guideline-directed medical therapy (GDMT). Unsafe suggestions occurred in complex device or advanced therapy decisions. Inter-rater agreement was high. CONCLUSIONS: ChatGPT-5 showed moderate concordance with ESC and ACC/AHA/HFSA HF guidelines, indicating potential value as a tool for knowledge synthesis and preliminary clinical support. However, its outputs require expert validation, and safe clinical integration will depend on future models incorporating guideline-based frameworks, real-time data, and rigorous physician oversight.