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

Yıl 2026, Cilt: 16 Sayı: 1, 42 - 48, 19.03.2026

Mehmet Zeki Erdem , Abdulaziz Yalınkılıç , Yaser Said Cetin , Nizamettin Erdem

https://izlik.org/JA48JP64GD

Öz

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.

Anahtar Kelimeler

Hearing Loss , Artificial Intelligence , ChatGPT , Gemini , Perplexity

Kaynakça

• 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