Korean J Orthod.  2025 Mar;55(2):131-141. 10.4041/kjod24.106.

Artificial intelligence solutions for temporomandibular joint disorders: Contributions and future potential of ChatGPT

Affiliations
  • 1Department of Orthodontics, Istanbul Galata University, Istanbul, Türkiye
  • 2Department of Prosthodontics, Uskudar University, Istanbul, Türkiye
  • 3Physical Medicine and Rehabilitation Clinic, Basaksehir Cam and Sakura City Hospital, Istanbul, Türkiye
  • 4Department of Physical Medicine and Rehabilitation, Golcuk Necati Celik State Hospital, Kocaeli, Türkiye

Abstract


Objective
This study aimed to evaluate the reliability and usefulness of information generated by Chat Generative Pre-Trained Transformer (ChatGPT) on temporomandibular joint disorders (TMD). Methods: We asked ChatGPT about the diseases specified in the TMD classification and scored the responses using Likert reliability and usefulness scales, the modified DISCERN (mDISCERN) scale, and the Global Quality Scale (GQS). Results: The highest Likert scores for both reliability and usefulness were for masticatory muscle disorders (mean ± standard deviation [SD]: 6.0 ± 0), and the lowest scores were for inflammatory disorders of the temporomandibular joint (mean ± SD: 4.3 ± 0.6 for reliability, 4.0 ± 0 for usefulness). The median Likert reliability score indicates that the responses are highly reliable. The median Likert usefulness score was 5 (4–6), indicating that the responses were moderately useful. A comparative analysis was performed, and no statistically significant differences were found in any subject for either reliability or usefulness (P = 0.083–1.000). The median mDISCERN score was 4 (3–5) for the two raters. A statistically significant difference was observed in the mean mDISCERN scores between the two raters (P = 0.046). The GQS scores indicated a moderate to high quality (mean ± SD: 3.8 ± 0.8 for rater 1, 4.0 ± 0.5 for rater 2). No statistically significant correlation was found between mDISCERN and GQS scores (r = –0.006, P = 0.980). Conclusions: Although ChatGPT-4 has significant potential, it can be used as an additional source of information regarding TMD for patients and clinicians.

Keyword

Artificial intelligence; ChatGPT; Temporomandibular joint disorders

Figure

  • Figure 1 Assessment of average reliability and usefulness scores of each disease (interquartile range error bars). TMJ, temporomandibular joint.


Reference

References

1. Andre A, Kang J, Dym H. 2022; Pharmacologic treatment for temporomandibular and temporomandibular joint disorders. Oral Maxillofac Surg Clin North Am. 34:49–59. https://doi.org/10.1016/j.coms.2021.08.001. DOI: 10.1016/j.coms.2021.08.001. PMID: 34598856.
2. Shaffer SM, Brismée JM, Sizer PS, Courtney CA. 2014; Temporomandibular disorders. Part 1: anatomy and examination/diagnosis. J Man Manip Ther. 22:2–12. https://doi.org/10.1179/2042618613y.0000000060. DOI: 10.1179/2042618613Y.0000000060. PMID: 24976743. PMCID: PMC4062347.
3. Thomas DC, Khan J, Manfredini D, Ailani J. 2023; Temporomandibular joint disorder comorbidities. Dent Clin North Am. 67:379–92. https://doi.org/10.1016/j.cden.2022.10.005. DOI: 10.1016/j.cden.2022.10.005. PMID: 36965938.
4. Valesan LF, Da-Cas CD, Réus JC, Denardin ACS, Garanhani RR, Bonotto D, et al. 2021; Prevalence of temporomandibular joint disorders: a systematic review and meta-analysis. Clin Oral Investig. 25:441–53. https://doi.org/10.1007/s00784-020-03710-w. DOI: 10.1007/s00784-020-03710-w. PMID: 33409693.
5. AlGhamdi KM, Moussa NA. 2012; Internet use by the public to search for health-related information. Int J Med Inform. 81:363–73. https://doi.org/10.1016/j.ijmedinf.2011.12.004. DOI: 10.1016/j.ijmedinf.2011.12.004. PMID: 22217800.
6. Mintz Y, Brodie R. 2019; Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 28:73–81. https://doi.org/10.1080/13645706.2019.1575882. DOI: 10.1080/13645706.2019.1575882. PMID: 30810430.
7. Kung JE, Marshall C, Gauthier C, Gonzalez TA, Jackson JB 3rd. 2023; Evaluating ChatGPT performance on the orthopaedic in-training examination. JB JS Open Access. 8:e23. https://doi.org/10.2106/jbjs.oa.23.00056. DOI: 10.2106/JBJS.OA.23.00056. PMID: 37693092. PMCID: PMC10484364. PMID: bec922f726d549bcbc0a81891f71a3b6.
8. Sharma S, Pajai S, Prasad R, Wanjari MB, Munjewar PK, Sharma R, et al. 2023; A critical review of ChatGPT as a potential substitute for diabetes educators. Cureus. 15:e38380. https://doi.org/10.7759/cureus.38380. DOI: 10.7759/cureus.38380. PMID: 37265899. PMCID: PMC10231273.
9. Palanica A, Flaschner P, Thommandram A, Li M, Fossat Y. 2019; Physicians' perceptions of chatbots in health care: cross-sectional web-based survey. J Med Internet Res. 21:e12887. https://doi.org/10.2196/12887. DOI: 10.2196/12887. PMID: 30950796. PMCID: PMC6473203.
10. Acar AH. 2024; Can natural language processing serve as a consultant in oral surgery? J Stomatol Oral Maxillofac Surg. 125:101724. https://doi.org/10.1016/j.jormas.2023.101724. DOI: 10.1016/j.jormas.2023.101724. PMID: 38052322.
11. Davenport T, Kalakota R. 2019; The potential for artificial intelligence in healthcare. Future Healthc J. 6:94–8. https://doi.org/10.7861/futurehosp.6-2-94. DOI: 10.7861/futurehosp.6-2-94. PMID: 31363513. PMCID: PMC6616181.
12. Ahmed N, Abbasi MS, Zuberi F, Qamar W, Halim MSB, Maqsood A, et al. 2021; Artificial intelligence techniques: analysis, application, and outcome in dentistry-a systematic review. Biomed Res Int. 2021:9751564. https://doi.org/10.1155/2021/9751564. DOI: 10.1155/2021/9751564. PMID: 34258283. PMCID: PMC8245240.
13. Alhaidry HM, Fatani B, Alrayes JO, Almana AM, Alfhaed NK. 2023; ChatGPT in dentistry: a comprehensive review. Cureus. 15:e38317. https://doi.org/10.7759/cureus.38317. DOI: 10.7759/cureus.38317. PMID: 37266053. PMCID: PMC10230850.
14. Eggmann F, Weiger R, Zitzmann NU, Blatz MB. 2023; Implications of large language models such as ChatGPT for dental medicine. J Esthet Restor Dent. 35:1098–102. https://doi.org/10.1111/jerd.13046. DOI: 10.1111/jerd.13046. PMID: 37017291.
15. Strunga M, Urban R, Surovková J, Thurzo A. 2023; Artificial intelligence systems assisting in the assessment of the course and retention of orthodontic treatment. Healthcare (Basel). 11:683. https://doi.org/10.3390/healthcare11050683. DOI: 10.3390/healthcare11050683. PMID: 36900687. PMCID: PMC10000479.
16. Schwendicke F, Samek W, Krois J. 2020; Artificial intelligence in dentistry: chances and challenges. J Dent Res. 99:769–74. https://doi.org/10.1177/0022034520915714. DOI: 10.1177/0022034520915714. PMID: 32315260. PMCID: PMC7309354.
17. Cankurtaran RE, Polat YH, Aydemir NG, Umay E, Yurekli OT. 2023; Reliability and usefulness of ChatGPT for inflammatory bowel diseases: an analysis for patients and healthcare professionals. Cureus. 15:e46736. https://doi.org/10.7759/cureus.46736. DOI: 10.7759/cureus.46736. PMID: 38022227. PMCID: PMC10630704.
18. Uz C, Umay E. 2023; "Dr ChatGPT": is it a reliable and useful source for common rheumatic diseases? Int J Rheum Dis. 26:1343–9. https://doi.org/10.1111/1756-185x.14749. DOI: 10.1111/1756-185X.14749. PMID: 37218530.
19. Hasnain M, Hayat A, Hussain A. 2023; Revolutionizing chronic obstructive pulmonary disease care with the open AI application: ChatGPT. Ann Biomed Eng. 51:2100–2. https://doi.org/10.1007/s10439-023-03238-6. DOI: 10.1007/s10439-023-03238-6. PMID: 37184746.
20. Chelli M, Descamps J, Lavoué V, Trojani C, Azar M, Deckert M, et al. 2024; Hallucination rates and reference accuracy of ChatGPT and bard for systematic reviews: comparative analysis. J Med Internet Res. 26:e53164. https://doi.org/10.2196/53164. DOI: 10.2196/53164. PMID: 38776130. PMCID: PMC11153973. PMID: ec1a9d0c24384d3d914d5c02f6fc50fa.
21. Alkaissi H, McFarlane SI. 2023; Artificial hallucinations in ChatGPT: implications in scientific writing. Cureus. 15:e35179. https://doi.org/10.7759/cureus.35179. DOI: 10.7759/cureus.35179. PMID: 36811129. PMCID: PMC9939079.
22. Yaltırık M, Palancıoğlu A, Koray M, Turgut CT. 2017; Temporomandibular joint disorders and diagnosis. Yeditepe J Dent. 13:43–50. https://doi.org/10.5505/yeditepe.2017.07078. DOI: 10.5505/yeditepe.2017.07078.
23. Bernard A, Langille M, Hughes S, Rose C, Leddin D, Veldhuyzen van Zanten S. 2007; A systematic review of patient inflammatory bowel disease information resources on the World Wide Web. Am J Gastroenterol. 102:2070–7. https://doi.org/10.1111/j.1572-0241.2007.01325.x. DOI: 10.1111/j.1572-0241.2007.01325.x. PMID: 17511753.
24. Agrawal P, Nikhade P. 2022; Artificial intelligence in dentistry: past, present, and future. Cureus. 14:e27405. https://doi.org/10.7759/cureus.27405. DOI: 10.7759/cureus.27405. PMID: 36046326. PMCID: PMC9418762.
25. Anil S, Sudeep K, Saratchandran S, Sweety VK. Chibinski ACR, editor. 2023. Revolutionizing dental caries diagnosis through artificial intelligence. Dental caries perspectives - a collection of thoughtful essays. IntechOpen;London: https://doi.org/10.5772/intechopen.112979. DOI: 10.5772/intechopen.112979.
26. Musleh D, Almossaeed H, Balhareth F, Alqahtani G, Alobaidan N, Altalag J, et al. 2024; Advancing dental diagnostics: a review of artificial intelligence applications and challenges in dentistry. Big Data Cogn Comput. 8:66. https://doi.org/10.3390/bdcc8060066. DOI: 10.3390/bdcc8060066. PMID: bd87ab5a23bf40a4b652cf010cbfc980.
27. Ghaffari M, Zhu Y, Shrestha A. 2024; A review of advancements of artificial intelligence in dentistry. Dent Rev. 4:100081. https://doi.org/10.1016/j.dentre.2024.100081. DOI: 10.1016/j.dentre.2024.100081.
28. Balaban C, Inam W, Kennedy R, Faiella R. 2021; The future of dentistry: how AI is transforming dental practices. Compend Contin Educ Dent. 42:14–7. https://pubmed.ncbi.nlm.nih.gov/33481621/.
29. Xie B, Xu D, Zou XQ, Lu MJ, Peng XL, Wen XJ. 2024; Artificial intelligence in dentistry: a bibliometric analysis from 2000 to 2023. J Dent Sci. 19:1722–33. https://doi.org/10.1016/j.jds.2023.10.025. DOI: 10.1016/j.jds.2023.10.025. PMID: 39035285. PMCID: PMC11259617.
30. Kukalakunta Y, Thunki P, Yellu RR. 2024; Integrating artificial intelligence in dental healthcare: opportunities and challenges. J Deep Learn Genom Data Anal. 4:34–41. https://aithor.com/paper-summary/integrating-artificial-intelligence-in-dental-healthcare-opportunities-and-challenges.
31. Kessels RP. 2003; Patients' memory for medical information. J R Soc Med. 96:219–22. https://doi.org/10.1177/014107680309600504. DOI: 10.1177/014107680309600504. PMID: 12724430. PMCID: PMC539473.
32. Vinufrancis A, Al Hussein H, Patel HV, Nizami A, Singh A, Nunez B, et al. 2024; Assessing the quality and reliability of AI-generated responses to common hypertension queries. Cureus. 16:e66041. https://doi.org/10.7759/cureus.66041. DOI: 10.7759/cureus.66041. PMID: 39224724. PMCID: PMC11366780.
33. Onder CE, Koc G, Gokbulut P, Taskaldiran I, Kuskonmaz SM. 2024; Evaluation of the reliability and readability of ChatGPT-4 responses regarding hypothyroidism during pregnancy. Sci Rep. 14:243. https://doi.org/10.1038/s41598-023-50884-w. DOI: 10.1038/s41598-023-50884-w. PMID: 38167988. PMCID: PMC10761760. PMID: cf05fd3fad374273985631cfef83c1fb.
34. Xie Y, Seth I, Hunter-Smith DJ, Rozen WM, Seifman MA. 2024; Investigating the impact of innovative AI chatbot on post-pandemic medical education and clinical assistance: a comprehensive analysis. ANZ J Surg. 94:68–77. https://doi.org/10.1111/ans.18666. DOI: 10.1111/ans.18666. PMID: 37602755.
35. Dursun D, Bilici Geçer R. 2024; Can artificial intelligence models serve as patient information consultants in orthodontics? BMC Med Inform Decis Mak. 24:211. https://doi.org/10.1186/s12911-024-02619-8. DOI: 10.1186/s12911-024-02619-8. PMID: 39075513. PMCID: PMC11285120. PMID: 1503e7a1dfbd46bea66e94c40ad14e5c.
36. Hatia A, Doldo T, Parrini S, Chisci E, Cipriani L, Montagna L, et al. 2024; Accuracy and completeness of ChatGPT-generated information on interceptive orthodontics: a multicenter collaborative study. J Clin Med. 13:735. https://doi.org/10.3390/jcm13030735. DOI: 10.3390/jcm13030735. PMID: 38337430. PMCID: PMC10856539.
37. Alan R, Alan BM. 2023; Utilizing ChatGPT-4 for providing information on periodontal disease to patients: a DISCERN quality analysis. Cureus. 15:e46213. https://doi.org/10.7759/cureus.46213. DOI: 10.7759/cureus.46213. PMID: 37908933. PMCID: PMC10613831.
38. Zengin O, Onder ME. 2021; Educational quality of YouTube videos on musculoskeletal ultrasound. Clin Rheumatol. 40:4243–51. https://doi.org/10.1007/s10067-021-05793-6. DOI: 10.1007/s10067-021-05793-6. PMID: 34059985. PMCID: PMC8166370.
39. Wadhwa S, Kapila S. 2008; TMJ disorders: future innovations in diagnostics and therapeutics. J Dent Educ. 72:930–47. https://pubmed.ncbi.nlm.nih.gov/18676802/. DOI: 10.1002/j.0022-0337.2008.72.8.tb04569.x. PMID: 18676802. PMCID: PMC2547984.
40. Balel Y. 2023; Can ChatGPT be used in oral and maxillofacial surgery? J Stomatol Oral Maxillofac Surg. 124:101471. https://doi.org/10.1016/j.jormas.2023.101471. DOI: 10.1016/j.jormas.2023.101471. PMID: 37061037.
41. Kılınç DD, Mansız D. 2024; Examination of the reliability and readability of Chatbot Generative Pretrained Transformer's (ChatGPT) responses to questions about orthodontics and the evolution of these responses in an updated version. Am J Orthod Dentofacial Orthop. 165:546–55. https://doi.org/10.1016/j.ajodo.2023.11.012. DOI: 10.1016/j.ajodo.2023.11.012. PMID: 38300168.
42. Tanaka OM, Gasparello GG, Hartmann GC, Casagrande FA, Pithon MM. 2023; Assessing the reliability of ChatGPT: a content analysis of self-generated and self-answered questions on clear aligners, TADs and digital imaging. Dental Press J Orthod. 28:e2323183. https://doi.org/10.1590/2177-6709.28.5.e2323183.oar. DOI: 10.1590/2177-6709.28.5.e2323183.oar. PMID: 37937680. PMCID: PMC10627416. PMID: fd1e68b950b544a185fcd2e408c01dc2.
43. Khanagar SB, Al-Ehaideb A, Vishwanathaiah S, Maganur PC, Patil S, Naik S, et al. 2021; Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making: a systematic review. J Dent Sci. 16:482–92. https://doi.org/10.1016/j.jds.2020.06.018. DOI: 10.1016/j.jds.2020.06.018. PMID: 33384781. PMCID: PMC7770311.
44. Abu Arqub S, Al-Moghrabi D, Allareddy V, Upadhyay M, Vaid N, Yadav S. 2024; Content analysis of AI-generated (ChatGPT) responses concerning orthodontic clear aligners. Angle Orthod. 94:263–72. https://doi.org/10.2319/062623-472.1. DOI: 10.2319/071123-484.1. PMID: 38195060. PMCID: PMC11050467.
45. Siontis KC, Attia ZI. 2024; ChatGPT hallucinating: can it get any more humanlike? Eur Heart J. 45:321–3. https://doi.org/10.1093/eurheartj/ehad548. DOI: 10.1093/eurheartj/ehad548. PMID: 37674408.
46. Sallam M. 2023; ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. Healthcare (Basel). 11:887. https://doi.org/10.3390/healthcare11060887. DOI: 10.3390/healthcare11060887. PMID: 36981544. PMCID: PMC10048148.
47. Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C. 2023; Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLOS Digit Health. 2:e0000198. https://doi.org/10.1371/journal.pdig.0000198. DOI: 10.1371/journal.pdig.0000198. PMID: 36812645. PMCID: PMC9931230. PMID: abc82f32e9d44f6c8570aadf34cfe565.
48. Fatima A, Shafi I, Afzal H, Díez IT, Lourdes DRM, Breñosa J, et al. 2022; Advancements in dentistry with artificial intelligence: current clinical applications and future perspectives. Healthcare (Basel). 10:2188. https://doi.org/10.3390/healthcare10112188. DOI: 10.3390/healthcare10112188. PMID: 36360529. PMCID: PMC9690084.
49. Vaira LA, Sergnese S, Salzano G, Maglitto F, Arena A, Carraturo E, et al. 2023; Are YouTube videos a useful and reliable source of information for patients with temporomandibular joint disorders? J Clin Med. 12:817. https://doi.org/10.3390/jcm12030817. DOI: 10.3390/jcm12030817. PMID: 36769466. PMCID: PMC9918192.
50. Gilson A, Safranek CW, Huang T, Socrates V, Chi L, Taylor RA, et al. 2023; How does ChatGPT perform on the United States medical licensing examination (USMLE)? The implications of large language models for medical education and knowledge assessment. JMIR Med Educ. 9:e45312. https://doi.org/10.2196/45312. DOI: 10.2196/45312. PMID: 36753318. PMCID: PMC9947764. PMID: 56efa8a062244f0898f11b12fa0d5d93.
Full Text Links
  • KJOD
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2025 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr