Journals / Eskisehir Medical Journal / 2026 / Volume: 7 Issue: 3

Comparative Evaluation of ChatGPT And Gemini Responses to Musculoskeletal Trauma

Fatma Hakyemez(Department of Medical Services and Techniques, First Aid Program, Vocational School of Health Sciences, Selcuk University)0000-0001-8390-087X
Emine Cihan(Department of Therapy and Rehabilitation, Physiotherapy Program, Vocational School of Health Sciences, Selcuk University)0000-0003-0699-3771
Cansu Şahbaz Pirinççi(Gulhane Faculty of Physiotherapy and Rehabilitation, University of Health Sciences)0000-0002-3921-0721
Pages
—
Publication Date
2026-08-29
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

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

The findings indicate an inverse relationship between the two models in terms of accuracy and readability.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Introduction: This research aimed to comparatively evaluate the performance of ChatGPT and Gemini in terms of medical accuracy and readability regarding first aid in musculoskeletal system traumas.
📊
Key Findings & Data:Methods: In the research, the responses given by both artificial intelligence systems to 10 first aid questions directed in the same format and in the Turkish language were analyzed.
💡
Conclusion & Impact:The findings indicate an inverse relationship between the two models in terms of accuracy and readability.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Artificial Intelligence · Clinical Decision Support · Emergency Care

Abstract

Introduction: This research aimed to comparatively evaluate the performance of ChatGPT and Gemini in terms of medical accuracy and readability regarding first aid in musculoskeletal system traumas. Methods: In the research, the responses given by both artificial intelligence systems to 10 first aid questions directed in the same format and in the Turkish language were analyzed. The responses were evaluated by 7 experts with clinical experience in the fields of first aid training and emergency health services. Flesch-Kincaid Grade Level scores were recorded for each response. Mann-Whitney U test was used in statistical analyses, and the significance level was accepted as p<0.05. Results: In questions 3, 4, and 6, Gemini’s response quality was found to be statistically significantly superior (p=0.035, p=0.035, and p=0.037). In the readability analysis, Flesch-Kincaid scores ranged between 9.85–13.46 for ChatGPT and 12.28–17.35 for Gemini. It was determined that ChatGPT responses were more readable, while Gemini responses required a higher reading level. The findings indicate an inverse relationship between the two models in terms of accuracy and readability. Conclusion: Artificial intelligence-powered chatbots hold significant public health potential for rapid access to first aid information. However, it has been observed that more accurate responses are not always more readable. Therefore, it is thought that optimizing both the accuracy and readability of artificial intelligence-based health information will contribute to its safe and effective use by the public.

🔖

Cite This Article

Hakyemez, F., Cihan, E., Pirinççi, C. Ş. (2026). Comparative Evaluation of ChatGPT And Gemini Responses to Musculoskeletal Trauma. Eskisehir Medical Journal, Volume: 7 Issue: 3. https://ajindex.com/articles/1614182

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (25)

  1. Ajmera P, Kharat A, Botchu R, Gupta H, Kulkarni V. Real-world analysis of artificial intelligence in musculoskeletal trauma. J Clin Orthop Trauma. 2021;22:101573. PMID: 34527511. DOI: https://doi.org/10.1016/j.jcot.2021.101573.
  2. World Health Organization. Falls; Disability and health [Internet]. Geneva: World Health Organization; 2021 Apr 26 [cited 2025 Oct 22]. Available at: https://www.who.int/news-room/fact-sheets/detail/falls; https://www.who.int/news-room/fact-sheets/detail/disability-and-health.
  3. Zech JR, Santomartino SM, Yi PH. Artificial intelligence (AI) for fracture diagnosis: An overview of current products and considerations for clinical adoption, from the AJR special series on AI applications. AJR Am J Roentgenol. 2022;219(6):869-78. PMID: 35731103. DOI: https://doi.org/10.2214/AJR.22.27873.
  4. Örsçelik A, Yıldız Y. Kas-iskelet sistemi yaralanmaları: aşırı kullanım yaralanmaları. Turkiye Klinikleri J Sports Med-Special Topics. 2015;1(3):62-69.
  5. Ercan S, Çetin C. Kas-iskelet sistemi yaralanmaları: alt ekstremite akut yaralanmaları. Turkiye Klinikleri J Sports Med-Special Topics. 2015;1(3):82-88.
  6. Uysal, M. (ed.) (2023). Spora Çok Yönlü Yaklaşım. Özgür Yayınları. DOI: https://doi.org/10.58830/ozgur.pub329.
  7. Baldwin AJ. Readability, accountability, and quality of burns first aid information available online. Burns. 2023;49(8):1823-1832. PMID: 37821277. DOI: https://doi.org/10.1016/j.burns.2023.03.002.
  8. Bushuven S, Bentele M, Bentele S, et al. ChatGPT, Can You Help Me Save My Child’s Life?” Diagnostic Accuracy and Supportive Capabilities to Lay Rescuers by ChatGPT in Prehospital Basic Life Support and Paediatric Advanced Life Support Cases – An In-silico Analysis. J Med Syst. 2023;21;47(1):123. PMID: 37987870. DOI: https://doi.org/10.1007/s10916-023-02019-x.
  9. Akalın B, Veranyurt Ü. Sağlık 4.0 ve sağlıkta yapay zekâ. J Health Prof Res. 2022;4(1):57-64.
  10. Yılmaz Y, Yılmaz DU, Yıldırım D, Akın E, Kaya DÖ. Artificial Intelligence and The Use of Artificial Intelligence in Health: Opinions of Health Sciences Students Health Sciences students. Suleyman Demirel Univ J Health Sci. 2021;12(3):297-308. DOI: https://doi.org/10.22312/sdusbed.950372.
  11. Hoşgör H, Güngördü H. A qualitative research on the artificial intelligence in health. EJOSAT. 2022;(35):395-407. DOI: https://doi.org/10.31590/ejosat.1052614.
  12. Birkun AA, Gautam A. Instructional support on first aid in choking by an artificial intelligence-powered chatbot. Am J Emerg Med. 2023;70:200-202. PMID: 37330383. DOI: https://doi.org/10.1016/j.ajem.2023.06.010.
  13. Birkun, AA, Gautam A. Large language model–based chatbot as a source of advice on first aid in heart attack. Curr Probl Cardiol. 2024;49(1):102048. PMID: 37640177. DOI: https://doi.org/10.1016/j.cpcardiol.2023.102048.
  14. Baldwin AJ. An artificial intelligence language model improves readability of burns first aid information. Burns. 2024;50(5):1122-1127. PMID: 38492982. DOI: https://doi.org/10.1016/j.burns.2024.03.005.
  15. Mika AP, Martin JR, Engstrom SM, et al. Assessing ChatGPT responses to common patient questions regarding total hip arthroplasty. JBone Joint Surg. 2023;105(19):1519–1526. DOI: 10.2106/JBJS.23.00209.
  16. Özduran E, Hancı V, Erkin Y, Özbek İC, Abdulkerimov V. Assessing the readability, quality and reliability of responses produced by ChatGPT, Gemini, and Perplexity regarding most frequently asked keywords about low back pain. PeerJ. 2025;13:e18847. DOI: https://doi.org/10.7717/peerj.18847.
  17. Sürme Y, Topan H, Maraş Baydoğan G. Howw succesful is AI in developing postsurgical wound care education matrerial? Wound Repair Regen. 2025;33(3):e70041. DOI: https://doi.org/10.1111/wrr.70041.
  18. Ömür Arça D, Erdemir İ, Kara F, et al. Assessing the readability, reliability, and quality of artificial intelligence chatbot responses to the 100 most searched queries about cardiopulmonary resuscitation: An observational study. Medicine (Baltimore). 2024;103(22):e38352. DOI: http://dx.doi.org/10.1097/MD.0000000000038352.
  19. Kara M, Özduran E, Kara MM, Özbek İC, Hancı V. Evaluating the readability, quality, and reliability of responses generated by ChatGPT, Gemini, and Perplexity on the most commonly asked questions about Ankylosing spondylitis. PLoS One. 2025;20(6):e0326351. DOI: https://doi.org/10.1371/journal.pone.0326351.
  20. Özduran E, Akkoc I, Büyükçoban S, Erkin Y, Hanci V. Readability, reliability and quality of responses generated by ChatGPT, gemini, and perplexity for the most frequently asked questions about pain. Medicine (Baltimore). 2025;104(11):e41780. DOI: 10.1097/MD.0000000000041780.
  21. Yau JY, Saadat S, Hsu E, et al. Accuracy of prospective assessments of 4 large language model chatbot responses to patient questions about emergency care: Experimental comparative study. J Med Internet Res. 2024;4;26:e60291. PMID: 39496149. DOI:10.2196/60291.
  22. Birkun AA, Gautam A. Large language model (LLM)-powered chatbots fail to generate guideline-consistent content on resuscitation and may provide potentially harmful advice. Prehosp Disaster Med. 2023;38(6):757-763. PMID: 37927093. DOI: 10.1017/S1049023X23006568.
  23. Birkun AA, Gautam A. Dr. google's advice on first aid: Evaluation of the search engine's question-answering system responses to queries seeking help in health emergencies. Prehosp Disaster Med. 2023;38(3):345-351. PMID: 37165838. DOI: 10.1017/S1049023X23000511.
  24. Troian M, Lovadina S, Ravasin A, et al. An assessment of ChatGPT’s responses to common patient questions about lung cancer surgery: A preliminary clinical evaluation of accuracy and relevance. J Clin Med. 2025;14(5):1676. PMID: 40095693. DOI: https://doi.org/10.3390/jcm14051676.
  25. Wrenn SP, Mika AP, Ponce RB, Mitchell PM. Evaluating ChatGPT's ability to answer common patient questions regarding hip fracture. J Am Acad Orthop Surg. 2024;32(14):656-659. PMID: 38743959. DOI: 10.5435/JAAOS-D-23-00877.