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Evaluating the Effectiveness of Artificial Intelligence Language Models in Hearing Loss Assessment: Comparative Study of ChatGPT, Gemini, and Perplexity

Year 2026, Volume: 16 Issue: 1, 42 - 48, 19.03.2026
https://izlik.org/JA48JP64GD

Abstract

Aim: Hearing loss constitutes a considerable health concern that adversely impacts individuals' quality of life. This study aims to compare the efficacy of artificial intelligence (AI) based methods in evaluating hearing loss.
Material and Method: The study assessed the performance of the ChatGPT, Gemini, and Perplexity programs based on quality (GQS), accuracy (Likert), and readability (GFI). The data were analyzed utilizing nonparametric tests, and the intergroup differences were assessed with the Kruskal-Wallis H test. Groups exhibiting substantial differences were analyzed using the Bonferroni-adjusted Post-Hoc test.
Results: The investigation indicates that ChatGPT outperforms other tools in quality (p=0.018). No substantial difference was seen between the groups regarding accuracy (p=0.072) and readability (p>0.05). The GQS score for ChatGPT was determined to be 4.93, for Gemini it was 4.71, and for Perplexity it was 4.43.
Conclusion: ChatGPT has exhibited enhanced performance regarding quality in the assessment of hearing loss. Nonetheless, comparable outcomes regarding accuracy and readability indicate that alternative approaches may also prove effective in some applications. These findings endorse the efficacy of AI-based technologies in particular health concerns, such as the evaluation of hearing loss. In the future, it is advisable to evaluate these technologies using larger samples and across various health conditions.

References

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  • 18. Herbold S, Hautli-Janisz A, Heuer U, Kikteva Z, Trautsch A. A large-scale comparison of human-written versus ChatGPTgenerated essays. Sci Rep. 2023;13(1):18617. https://doi. org/10.1038/s41598-023-45644-9
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  • 20. Moons P, Van Bulck L. Using ChatGPT and Google Bard to improve the readability of written patient information: a proof of concept. Eur J Cardiovasc Nurs. 2024;23(2):122–126. https://doi.org/10.1093/eurjcn/zvad087
  • 21. Maleki Varnosfaderani S, Forouzanfar M. The role of AI in hospitals and clinics: Transforming healthcare in the 21st century. Bioengineering (Basel). 2024;11(4):337. https://doi. org/10.3390/bioengineering11040337

Year 2026, Volume: 16 Issue: 1, 42 - 48, 19.03.2026
https://izlik.org/JA48JP64GD

Abstract

References

  • 1. World Health Organization. Deafness and hearing loss. Geneva:World Health Organization; 2023 [Accessed: 17.05.2025]. https://www.who.int
  • 2. Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396(10248):413–446. https://doi.org/10.1016/S0140-6736(20)30367-6
  • 3. Ciorba A, Bianchini C, Pelucchi S, Pastore A. The impact of hearing loss on the quality of life of elderly adults. Clin Interv Aging. 2012;7:159–163. https://doi.org/10.2147/CIA. S26059
  • 4. Wilson BS, Tucci DL, Merson MH, O’Donoghue GM. Global hearing health care: New findings and perspectives. Lancet. 2017;390(10111):2503–2515. https://doi.org/10.1016/ S0140-6736(17)31073-5
  • 5. Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2021;396(10258):1204–1222. https://doi.org/10.1016/S0140-6736(20)30925-9
  • 6. Lin FR, Metter EJ, O’Brien RJ, Resnick SM, Zonderman AB, Ferrucci L. Hearing loss and incident dementia. Arch Neurol. 2011;68(2):214–220. https://doi.org/10.1001/archneurol.2010.362
  • 7. Olusanya BO, Davis AC, Hoffman HJ. Hearing loss:Rising prevalence and impact. Bull World Health Organ. 2019;97(10):646–646A. https://doi.org/10.2471/BLT.19.224683
  • 8. Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. https://doi.org/10.1038/s41591-018-0300-7
  • 9. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M,Chou K, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24–29. https://doi.org/10.1038/s41591-018- 0316-z
  • 10. Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys. 2019;29(2):102–127. https://doi.org/10.1016/j.zemedi.2018.11.002
  • 11. Seo HW, Oh YJ, Oh J, Lee DK, Lee SH, Chung JH, et al. Prediction of hearing recovery with deep learning algorithm in sudden sensorineural hearing loss. Sci Rep. 2024;14(1):20058. https://doi.org/10.1038/s41598-024-70436-0
  • 12. Gudapati JD, Franco AJ, Tamang S, Mikhael A, Hadi MA, Roy V, et al. A study of global quality scale and reliability scores for chest pain: an Instagram-post analysis. Cureus. 2023;15(9):e45629. https://doi.org/10.7759/cureus.45629
  • 13. Jebb AT, Ng V, Tay L. A review of key Likert scale development advances: 1995-2019. Front Psychol. 2021;12:637547. https://doi.org/10.3389/fpsyg.2021.637547
  • 14. Świeczkowski D, Kułacz S. The use of the Gunning Fog Index to evaluate the readability of Polish and English drug leaflets in the context of Health Literacy challenges in Medical Linguistics: an exploratory study. Cardiol J. 2021;28(4):627–631. https://doi.org/10.5603/CJ.a2020.0142
  • 15. Brown TB, Mann B, Ryder N, Subbiah M, Kaplan J, Dhariwal P, et al. Language models are few-shot learners. Adv Neural Inf Process Syst. 2020;33:1877–1901. https://doi.org/10.48550/ arXiv.2005.14165
  • 16. Moazemi S, Vahdati S, Li J, Kalkhoff S, Castano LJ, Dewitz B, et al. Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review. Front Med (Lausanne). 2023;10:1109411. https://doi.org/10.3389/fmed.2023.1109411
  • 17. Rashid MM, Atilgan N, Dobres J, Day S, Penkova V, Küçük M, et al. Humanizing AI in education: A readability comparisonof LLM and human-created educational content. Proc Hum Factors Ergon Soc Annu Meet. 2024;68(1):596–603. https://doi.org/10.1177/10711813241261689
  • 18. Herbold S, Hautli-Janisz A, Heuer U, Kikteva Z, Trautsch A. A large-scale comparison of human-written versus ChatGPTgenerated essays. Sci Rep. 2023;13(1):18617. https://doi. org/10.1038/s41598-023-45644-9
  • 19. Khaja H. Using AI language models to simplify patient education materials. Rheumatology Advisor; 2023. [Accessed:17.05.2025] https://www.rheumatologyadvisor.com/features/ use-of-ai-to-create-patient-education-materials/
  • 20. Moons P, Van Bulck L. Using ChatGPT and Google Bard to improve the readability of written patient information: a proof of concept. Eur J Cardiovasc Nurs. 2024;23(2):122–126. https://doi.org/10.1093/eurjcn/zvad087
  • 21. Maleki Varnosfaderani S, Forouzanfar M. The role of AI in hospitals and clinics: Transforming healthcare in the 21st century. Bioengineering (Basel). 2024;11(4):337. https://doi. org/10.3390/bioengineering11040337
There are 21 citations in total.

Details

Primary Language English
Subjects Otorhinolaryngology
Journal Section Research Article
Authors

Mehmet Zeki Erdem

Abdulaziz Yalınkılıç

Yaser Said Cetin

Nizamettin Erdem

Submission Date June 2, 2025
Acceptance Date September 15, 2025
Publication Date March 19, 2026
IZ https://izlik.org/JA48JP64GD
Published in Issue Year 2026 Volume: 16 Issue: 1

Cite

APA Erdem, M. Z., Yalınkılıç, A., Cetin, Y. S., & Erdem, N. (2026). Evaluating the Effectiveness of Artificial Intelligence Language Models in Hearing Loss Assessment: Comparative Study of ChatGPT, Gemini, and Perplexity. Kafkas Journal of Medical Sciences, 16(1), 42-48. https://izlik.org/JA48JP64GD
AMA 1.Erdem MZ, Yalınkılıç A, Cetin YS, Erdem N. Evaluating the Effectiveness of Artificial Intelligence Language Models in Hearing Loss Assessment: Comparative Study of ChatGPT, Gemini, and Perplexity. Kafkas Journal of Medical Sciences. 2026;16(1):42-48. https://izlik.org/JA48JP64GD
Chicago Erdem, Mehmet Zeki, Abdulaziz Yalınkılıç, Yaser Said Cetin, and Nizamettin Erdem. 2026. “Evaluating the Effectiveness of Artificial Intelligence Language Models in Hearing Loss Assessment: Comparative Study of ChatGPT, Gemini, and Perplexity”. Kafkas Journal of Medical Sciences 16 (1): 42-48. https://izlik.org/JA48JP64GD.
EndNote Erdem MZ, Yalınkılıç A, Cetin YS, Erdem N (March 1, 2026) Evaluating the Effectiveness of Artificial Intelligence Language Models in Hearing Loss Assessment: Comparative Study of ChatGPT, Gemini, and Perplexity. Kafkas Journal of Medical Sciences 16 1 42–48.
IEEE [1]M. Z. Erdem, A. Yalınkılıç, Y. S. Cetin, and N. Erdem, “Evaluating the Effectiveness of Artificial Intelligence Language Models in Hearing Loss Assessment: Comparative Study of ChatGPT, Gemini, and Perplexity”, Kafkas Journal of Medical Sciences, vol. 16, no. 1, pp. 42–48, Mar. 2026, [Online]. Available: https://izlik.org/JA48JP64GD
ISNAD Erdem, Mehmet Zeki - Yalınkılıç, Abdulaziz - Cetin, Yaser Said - Erdem, Nizamettin. “Evaluating the Effectiveness of Artificial Intelligence Language Models in Hearing Loss Assessment: Comparative Study of ChatGPT, Gemini, and Perplexity”. Kafkas Journal of Medical Sciences 16/1 (March 1, 2026): 42-48. https://izlik.org/JA48JP64GD.
JAMA 1.Erdem MZ, Yalınkılıç A, Cetin YS, Erdem N. Evaluating the Effectiveness of Artificial Intelligence Language Models in Hearing Loss Assessment: Comparative Study of ChatGPT, Gemini, and Perplexity. Kafkas Journal of Medical Sciences. 2026;16:42–48.
MLA Erdem, Mehmet Zeki, et al. “Evaluating the Effectiveness of Artificial Intelligence Language Models in Hearing Loss Assessment: Comparative Study of ChatGPT, Gemini, and Perplexity”. Kafkas Journal of Medical Sciences, vol. 16, no. 1, Mar. 2026, pp. 42-48, https://izlik.org/JA48JP64GD.
Vancouver 1.Mehmet Zeki Erdem, Abdulaziz Yalınkılıç, Yaser Said Cetin, Nizamettin Erdem. Evaluating the Effectiveness of Artificial Intelligence Language Models in Hearing Loss Assessment: Comparative Study of ChatGPT, Gemini, and Perplexity. Kafkas Journal of Medical Sciences [Internet]. 2026 Mar. 1;16(1):42-8. Available from: https://izlik.org/JA48JP64GD