1. Nazir A, Wang Z. A comprehensive survey of ChatGPT: Advancements, applications, prospects, and challenges. Meta-Radiology. 2023;1:100022.

https://doi.org/10.1016/j.metrad.2023.100022

PMid:37901715 PMCid:PMC10611551

2. Wang J, Deng H, Liu B, Hu A, Liang J, Fan L, et al. Systematic evaluation of research progress on natural language processing in medicine over the past 20 years: Bibliometric study on pubmed. J Med Internet Res. 2020;22:e16816.

https://doi.org/10.2196/16816

PMid:32012074 PMCid:PMC7005695

3. Li J, Dada A, Puladi B, Kleesiek J, Egger J. ChatGPT in healthcare: A taxonomy and systematic review. Comput Methods Programs Biomed. 2024;245:108013.

https://doi.org/10.1016/j.cmpb.2024.108013

PMid:38262126 

4. Dave T, Athaluri SA, Singh S. ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations. Front Artif Intell. 2023;6:1169595.

https://doi.org/10.3389/frai.2023.1169595

PMid:37215063 PMCid:PMC10192861

5. Montazeri M, Galavi Z, Ahmadian L. What are the applications of ChatGPT in healthcare: Gain or loss? Heal Sci Reports. 2024;7:e1878.

https://doi.org/10.1002/hsr2.1878

PMid:38361810 PMCid:PMC10867364

6. Garg RK, Urs VL, Agarwal AA, Chaudhary SK, Paliwal V, Kar SK. Exploring the role of ChatGPT in patient care (diagnosis and treatment) and medical research: A systematic review. Heal Promot Perspect. 2023;13:183-91.

https://doi.org/10.34172/hpp.2023.22

PMid:37808939 PMCid:PMC10558973

7. Hirosawa T, Kawamura R, Harada Y, Mizuta K, Tokumasu K, Kaji Y, et al. ChatGPT-Generated Differential Diagnosis Lists for Complex Case-Derived Clinical Vignettes: Diagnostic Accuracy Evaluation. JMIR Med Informatics. 2023;11:e48808.

https://doi.org/10.2196/48808

PMid:37812468 PMCid:PMC10594139

8. Messadi D V. Diagnostic aids for detection of oral precancerous conditions. Int J Oral Sci. 2013;5:59-65.

https://doi.org/10.1038/ijos.2013.24

PMid:23743617 PMCid:PMC3707069

9. Yardimci G, Kutlubay Z, Engin B, Tuzun Y. Precancerous lesions of oral mucosa. World J Clin Cases. 2014;2:866-72.

https://doi.org/10.12998/wjcc.v2.i12.866

PMid:25516862 PMCid:PMC4266835

10. Steele TO, Meyers A. Early detection of premalignant lesions and oral cancer. Otolaryngol Clin North Am. 2011;44:221-9.

https://doi.org/10.1016/j.otc.2010.10.002

PMid:21093631 

11. Diamanti N, Duxbury AJ, Ariyaratnam S, Macfarlane TV. Attitudes to biopsy procedures in general dental practice. Br Dent J. 2002;192:588-92.

https://doi.org/10.1038/sj.bdj.4801434

PMid:12075959 

12. Tarakji B. Dentists' Perception of Oral Potentially Malignant Disorders. Int Dent J. 2022;72:414-9.

https://doi.org/10.1016/j.identj.2022.01.004

PMid:35227496 PMCid:PMC9275301

13. Khong B, Ferlito S, Quek S, Conte G, Ingrassia A, Lechien JR, et al. Past, Present, and Future Diagnostic Methods for the Early Noninvasive Detection of Oral Premalignant Lesions: A State of the Art and Systematic Review. Ear, Nose Throat J. 2024;0:1455613241245204.

https://doi.org/10.1177/01455613241245204

PMid:38695398 

14. Diniz-Freitas M, Rivas-Mundiña B, García-Iglesias JR, García-Mato E, Diz-Dios P. How ChatGPT performs in Oral Medicine: The case of oral potentially malignant disorders. Oral Dis. 2023;30:1912-8.

https://doi.org/10.1111/odi.14750

PMid:37794649 

15. Hassona Y, Alqaisi D, AL-Haddad A, Georgakopoulou EA, Malamos D, Alrashdan MS, et al. How good is ChatGPT at answering patients' questions related to early detection of oral (mouth) cancer? Oral Surg Oral Med Oral Pathol Oral Radiol. 2024;138:269-78.

https://doi.org/10.1016/j.oooo.2024.04.010

PMid:38714483 

16. Warnakulasuriya S, Kujan O, Aguirre-Urizar JM, Bagan J V., González-Moles MÁ, Kerr AR, et al. Oral potentially malignant disorders: A consensus report from an international seminar on nomenclature and classification, convened by the WHO Collaborating Centre for Oral Cancer. Oral Dis. 2021;27:1862-80.

https://doi.org/10.1111/odi.13704

PMid:33128420 

17. Liu M, Okuhara T, Chang XY, Shirabe R, Nishiie Y, Okada H, et al. Performance of ChatGPT Across Different Versions in Medical Licensing Examinations Worldwide: Systematic Review and Meta-Analysis. J Med Internet Res. 2024;26:e60807.

https://doi.org/10.2196/60807

PMid:39052324 PMCid:PMC11310649

18. Datta M, Sinha R, Sen S, Jha H, Deb D. Proliferative verrucus leukoplakia: A case series. J Fam Med Prim Care. 2022;11:3352-5.

https://doi.org/10.4103/jfmpc.jfmpc_646_21

PMid:36119284 PMCid:PMC9480668

19. Kristofelc N, Zidar N, Strojan P. Oral verrucous carcinoma: A diagnostic and therapeutic challenge. Radiol Oncol. 2023;57:1-11.

https://doi.org/10.2478/raon-2023-0015

PMid:36942907 PMCid:PMC10039467

20. Shavit E, Klieb H, Shear NH. Oral lichen planus: A novel staging and algorithmic approach and all that is essential to know. F1000Research. 2020;9:F1000 Faculty Rev-206.

https://doi.org/10.12688/f1000research.18713.1

PMid:32226613 PMCid:PMC7096219

21. De Angelis L, Baglivo F, Arzilli G, Privitera GP, Ferragina P, Tozzi AE, et al. ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health. Front Public Heal. 2023;11:1166120.

https://doi.org/10.3389/fpubh.2023.1166120

PMid:37181697 PMCid:PMC10166793

22. Shukla R, Mishra AK, Banerjee N, Verma A. The Comparison of ChatGPT 3.5, Microsoft Bing, and Google Gemini for Diagnosing Cases of Neuro-Ophthalmology. Cureus. 2024;16:e58232.

https://doi.org/10.7759/cureus.58232

PMid:38745784

23. Abdullah R, Fakieh B. Health care employees' perceptions of the use of artificial intelligence applications: Survey study. J Med Internet Res. 2020;22:e17620.

https://doi.org/10.2196/17620

PMid:32406857 PMCid:PMC7256754

24. Al-Medfa MK, Al-Ansari AMS, Darwish AH, Qreeballa TA, Jahrami H. Physicians' attitudes and knowledge toward artificial intelligence in medicine: Benefits and drawbacks. Heliyon. 2023;9:e14744.

https://doi.org/10.1016/j.heliyon.2023.e14744

PMid:37035387 PMCid:PMC10073828

25. Islam A, Banerjee A, Wati SM, Banerjee S, Shrivastava D, Srivastava KC. Utilizing Artificial Intelligence Application for Diagnosis of Oral Lesions and Assisting Young Oral Histopathologist in Deriving Diagnosis from Provided Features - A Pilot study. J Pharm Bioallied Sci. 2024;16:S1136-9.

https://doi.org/10.4103/jpbs.jpbs_1287_23

PMid:38882904 PMCid:PMC11174333

26. Fritsch SJ, Blankenheim A, Wahl A, Hetfeld P, Maassen O, Deffge S, et al. Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients. Digit Heal. 2022;8:20552076221116772.

https://doi.org/10.1177/20552076221116772

PMid:35983102 PMCid:PMC9380417

27. Wubineh BZ, Deriba FG, Woldeyohannis MM. Exploring the opportunities and challenges of implementing artificial intelligence in healthcare: A systematic literature review. Urol Oncol Semin Orig Investig. 2024;42:48-56.

https://doi.org/10.1016/j.urolonc.2023.11.019

PMid:38101991 

28. Seyyed-Kalantari L, Zhang H, McDermott MBA, Chen IY, Ghassemi M. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat Med. 2021;27:2176-82.

https://doi.org/10.1038/s41591-021-01595-0

PMid:34893776 PMCid:PMC8674135

29. Sengupta N, Sarode SC, Sarode GS, Ghone U. Scarcity of publicly available oral cancer image datasets for machine learning research. Oral Oncol. 2022;126:105737.

https://doi.org/10.1016/j.oraloncology.2022.105737

PMid:35114612 

30. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Futur Healthc J. 2019;6:94-8.

https://doi.org/10.7861/futurehosp.6-2-94

PMid:31363513 PMCid:PMC6616181