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Journals / Journal of Health Sciences and Medicine / 2026 / Volume: 9 Issue: 3

Artificial Intelligence-assisted patient education in cardiovascular surgery practice: comparative analysis of ChatGPT performance in Turkish and English languages

Abdul Kerim Buğra(Sağlık Bilimleri Üniversitesi Mehmet Akif Ersoy Göğüs Kalp ve Damar Cerrahisi Eğitim ve Araştırma Hastanesi)0000-0001-9575-0100
Pages
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Publication Date
2026-05-19
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Turkish-speaking patients may benefit from superior accessibility in AI-generated health information, with potential impact on preoperative education quality, informed consent processes, and postoperative patient engagement.

📌 3 Key Takeaways

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Objective & Hypothesis:Aims: This study aimed to systematically evaluate the clinical accuracy and patient accessibility of ChatGPT-4-generated cardiovascular surgery patient education content across Turkish and English languages, examining language-dependent performance patterns and their implications for clinical practice.
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Key Findings & Data:Methods: Twelve cardiovascular surgery specialists with minimum 5 years clinical experience conducted blinded evaluation of 52 ChatGPT-4 responses (26 Turkish, 26 English), generating 624 total expert assessments, across six cardiovascular surgery patient education domains using validated 7-point Likert scales.
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Conclusion & Impact:Turkish-speaking patients may benefit from superior accessibility in AI-generated health information, with potential impact on preoperative education quality, informed consent processes, and postoperative patient engagement.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Cardiovascular surgery · Artificial Intelligence · patient education

Abstract

Aims: This study aimed to systematically evaluate the clinical accuracy and patient accessibility of ChatGPT-4-generated cardiovascular surgery patient education content across Turkish and English languages, examining language-dependent performance patterns and their implications for clinical practice. Methods: Twelve cardiovascular surgery specialists with minimum 5 years clinical experience conducted blinded evaluation of 52 ChatGPT-4 responses (26 Turkish, 26 English), generating 624 total expert assessments, across six cardiovascular surgery patient education domains using validated 7-point Likert scales. Simultaneously, computational accessibility analysis was performed using language-specific readability indices. Statistical analysis included effect size calculations and correlation analysis. Results: ChatGPT-4 demonstrated high clinical accuracy across both languages (mean satisfaction: 6.40/7.0, 91.4%), with 88.7% of responses receiving high expert ratings. However, profound language-dependent accessibility disparities emerged; Turkish responses achieved near-universal patient accessibility (mean score: 77.7±4.2), while English responses required university-level education (mean score: 30.2±6.1), creating a 47.5-point accessibility gap (Cohen’s d=9.07, p<0.001). Turkish showed balanced clinical accuracy-patient accessibility optimization, while English exhibited systematic optimization prioritizing expert preferences over patient accessibility. Conclusion: Artificial Intelligence (AI)-generated cardiovascular surgery patient education shows equivalent clinical accuracy but profound accessibility disparities across languages. Turkish-speaking patients may benefit from superior accessibility in AI-generated health information, with potential impact on preoperative education quality, informed consent processes, and postoperative patient engagement.

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Cite This Article

Buğra, A. K. (2026). Artificial Intelligence-assisted patient education in cardiovascular surgery practice: comparative analysis of ChatGPT performance in Turkish and English languages. Journal of Health Sciences and Medicine, Volume: 9 Issue: 3. https://doi.org/10.32322/jhsm.1891905

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References (25)

  1. Dias RD, Shah JA, Zenati MA. Artificial Intelligence in cardiothoracic surgery. Minerva Cardioangiol. 2020;68(5):532-538. doi:10.23736/S0026-4725.20.05235-4
  2. Clark SC. Can ChatGPT transform cardiac surgery and heart transplantation? J Cardiothorac Surg. 2024;19(1):108. doi:10.1186/s13019-024-02541-0
  3. Institute of Medicine. Health Literacy: A Prescription to End Confusion. National Academy Press; 2004. doi:10.17226/10883
  4. Nutbeam D. Health literacy as a public health goal: a challenge for contemporary health education and communication strategies into the 21st century. Health Promot Int. 2000;15(3):259-267. doi:10.1093/heapro/15.3.259
  5. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. doi:10.1038/s41591-018-0300-7
  6. Benjamin EJ, Muntner P, Alonso A, et al. Heart disease and stroke statistics-2019 update: a report from the American Heart Association. Circulation. 2019;139(10):e56-e528. doi:10.1161/CIR.0000000000000659
  7. Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW. Large language models in medicine. Nat Med. 2023;29(8):1930-1940. doi:10.1038/s41591-023-02448-8
  8. Singhal K, Azizi S, Tu T, et al. Large language models encode clinical knowledge. Nature. 2023;620(7972):172-180. doi:10.1038/s41586-023-06291-2
  9. Leon M, Ruaengsri C, Pelletier G, et al. Harnessing the power of ChatGPT in cardiovascular medicine: innovations, challenges, and future directions. J Clin Med. 2024;13(21):6543. doi:10.3390/jcm13216543
  10. Rizwan A, Sadiq T. The use of AI in diagnosing diseases and providing management plans: a consultation on cardiovascular disorders with ChatGPT. Cureus. 2023;15(8):e43106. doi:10.7759/cureus.43106
  11. Rouhi AD, Ghanem YK, Yolchieva L, et al. Can artificial intelligence improve the readability of patient education materials on aortic stenosis? A pilot study. Cardiol Ther. 2024;13(1):137-147. doi:10.1007/s40119-023-00347-0
  12. Bhushan R, Grover V. The advent of artificial intelligence into cardiac surgery: a systematic review of our understanding. Braz J Cardiovasc Surg. 2024;39(5):e20230308. doi:10.21470/1678-9741-2023-0308
  13. Williams DR, Lawrence JA, Davis BA, Vu C. Understanding how discrimination can affect health. Health Serv Res. 2019;54(Suppl 2):1374-1388. doi:10.1111/1475-6773.13222
  14. Braveman P, Arkin E, Orleans T, Proctor D, Plough A. What Is Health Equity? And What Difference Does a Definition Make? Robert Wood Johnson Foundation; 2017.
  15. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-453. doi:10.1126/science.aax2342
  16. Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring fairness in machine learning to advance health equity. Ann Intern Med. 2018;169(12):866-872. doi:10.7326/M18-1990
  17. Veinot TC, Mitchell H, Ancker JS. Good intentions are not enough: how informatics interventions can worsen inequality. J Am Med Inform Assoc. 2018;25(8):1080-1088. doi:10.1093/jamia/ocy052
  18. Guillemin F, Bombardier C, Beaton D. Cross-cultural adaptation of health-related quality of life measures: literature review and proposed guidelines. J Clin Epidemiol. 1993;46(12):1417-1432. doi:10.1016/0895-4356(93)90142-N
  19. Lynn MR. Determination and quantification of content validity. Nurs Res. 1986;35(6):382-385. doi:10.1097/00006199-198611000-00017
  20. Flesch R. A new readability yardstick. J Appl Psychol. 1948;32(3):221-233. doi:10.1037/h0057532
  21. Ateşman E. Türkçede okunabilirliğin ölçülmesi. Ankara Üniv TÖMER Dil Derg. 1997;58:171-174.
  22. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Lawrence Erlbaum Associates; 1988. doi:10.4324/9780203771587
  23. Rajkomar A, Dean J, Kohane IS. Machine learning in medicine. N Engl J Med. 2019;380(14):1347-1358. doi:10.1056/NEJMra1814259
  24. Zack T, Lehman E, Suzgun M, et al. Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study. Lancet Digit Health. 2024;6(1):e12-e22. doi:10.1016/S2589-7500(23)00225-X
  25. Hanna JJ, Wakene AD, Johnson AO, Lehmann CU, Medford RJ. Assessing racial and ethnic bias in text generation by large language models for health care-related tasks: cross-sectional study. J Med Internet Res. 2025;27:e57257. doi:10.2196/57257