Calibrating Patient Trust Through Dentist Communication in AI-Assisted Dental Imaging: A Qualitative Study

Li Songjian; Cindy Denhara Wijaya; Shieny

doi:10.61919/yx8aes59

Authors

Li Songjian Universitas Prima Indonesia, Indonesia Author
Cindy Denhara Wijaya Universitas Prima Indonesia, Indonesia Author
Shieny Universitas Prima Indonesia, Indonesia Author

DOI:

https://doi.org/10.61919/yx8aes59

Keywords:

artificial intelligence, dental imaging, patient trust, dentist communication, explainability, qualitative research

Abstract

Background: Artificial intelligence is increasingly used to support interpretation of dental radiographic images, but diagnostic performance alone does not determine whether patients consider AI-supported recommendations trustworthy. Trust may depend on how visual outputs, uncertainty, professional accountability, privacy, and treatment consequences are communicated. Qualitative investigation is needed to understand how patients interpret these factors during AI-assisted dental care. Objective: To explore how adults perceive trust in AI-assisted dental imaging, how dentist communication shapes confidence and willingness to act, and which contextual factors influence acceptable reliance on AI-supported recommendations. Methods: An interpretive qualitative study informed by a constructivist orientation was conducted in an urban dental-care setting in Medan, Indonesia, between January and May 2026. Twelve adults aged 24–58 years with previous experience of AI-assisted dental imaging participated in semi-structured individual interviews. Purposive variation sampling sought diversity in gender, education, AI awareness, imaging experience, and dental anxiety. Data were examined using reflexive thematic analysis. Results: Three interconnected themes were developed: seeing strengthens belief but does not prove correctness; the dentist remains the trusted interpreter; and trust is conditional on accountability, privacy, and consequence. AI-supported visualization helped participants understand where to focus but did not replace clinical explanation. Trust increased when dentists contextualized findings, acknowledged uncertainty, invited questions, discussed alternatives, and retained responsibility for recommendations. Privacy, fairness, commercial motives, and treatment reversibility also influenced acceptable reliance, with participants seeking stronger human verification before irreversible care. Conclusion: Patient trust in AI-assisted dental imaging was conditional rather than automatic. Appropriate implementation should emphasize transparent clinician interpretation, uncertainty communication, professional accountability, patient choice, and proportionate human verification for higher-consequence decisions

References

1. Applications of Artificial Intelligence in Dentomaxillofacial Imaging Review Group. Applications of artificial intelligence in dentomaxillofacial imaging: a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol. 2024;138(5):641–655. doi:10.1016/j.oooo.2023.12.790.

2. Arzani S, Karimi A, Iranmanesh P, Yazdi M, Sabeti MA, Nekoofar MH, et al. Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of imaging modalities: an umbrella review with meta-analysis. PLoS One. 2025;20(8):e0329986. doi:10.1371/journal.pone.0329986.

3. Carvalho BKG, Nolden EL, Wenning AS, Kiss-Dala S, Agócs G, Róth I, et al. Diagnostic accuracy of artificial intelligence for approximal caries on bitewing radiographs: a systematic review and meta-analysis. J Dent. 2024;151:105388. doi:10.1016/j.jdent.2024.105388.

4. Borys K, Schmitt YA, Nauta M, Seifert C, Krämer N, Friedrich CM, et al. Explainable AI in medical imaging: an overview for clinical practitioners—saliency-based XAI approaches. Eur J Radiol. 2023;162:110787. doi:10.1016/j.ejrad.2023.110787.

5. Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021;3(11):e745–e750. doi:10.1016/S2589-7500(21)00208-9.

6. Jung J, Lee H, Jung H, Kim H. Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: a systematic review. Heliyon. 2023;9(5):e16110. doi:10.1016/j.heliyon.2023.e16110.

7. Muhammad D, Bendechache M. Unveiling the black box: a systematic review of explainable artificial intelligence in medical image analysis. Comput Struct Biotechnol J. 2024;24:542–560. doi:10.1016/j.csbj.2024.08.005.

8. Nong P, Ji M. Expectations of healthcare AI and the role of trust: understanding patient views on how AI will impact cost, access, and patient–provider relationships. J Am Med Inform Assoc. 2025;32(5):795–799. doi:10.1093/jamia/ocaf031.

9. Shevtsova D, Ahmed A, Boot IWA, Sanges C, Hudecek M, Jacobs JJL, et al. Trust in and acceptance of artificial intelligence applications in medicine: mixed methods study. JMIR Hum Factors. 2024;11:e47031. doi:10.2196/47031.

10. Woode ME, De Silva Perera U, Degeling C, Aquino YSJ, Houssami N, Carter SM, et al. Preferences for the use of artificial intelligence for breast cancer screening in Australia: a discrete choice experiment. Patient. 2025;18:495–510. doi:10.1007/s40271-025-00742-w.

11. Ho JCY, Chai HH, Lo ECM, Huang MZ, Chu CH. Strategies for effective dentist–patient communication: a literature review. Patient Prefer Adherence. 2024;18:1385–1394. doi:10.2147/PPA.S465221.

12. Ho JCY, Chai HH, Luo BW, Lo ECM, Huang MZ, Chu CH. An overview of dentist–patient communication in quality dental care. Dent J (Basel). 2025;13(1):31. doi:10.3390/dj13010031.

13. European Union. Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Off J Eur Union. 2024.

14. World Health Organization. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: World Health Organization; 2021.

15. World Health Organization. Regulatory considerations on artificial intelligence for health. Geneva: World Health Organization; 2023.

16. Braun V, Clarke V. One size fits all? What counts as quality practice in reflexive thematic analysis? Qual Res Psychol. 2021;18(3):328–352. doi:10.1080/14780887.2020.1769238.

17. Braun V, Clarke V. Conceptual and design thinking for thematic analysis. Qual Psychol. 2022;9(1):3–26. doi:10.1037/qup0000196.

18. Braun V, Clarke V. Thematic analysis: a practical guide. London: SAGE; 2022.

19. Dossett LA, Kaji AH, Cochran A. SRQR and COREQ reporting guidelines for qualitative studies. JAMA Surg. 2021;156(9):875–876. doi:10.1001/jamasurg.2021.0525.

20. Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–357. doi:10.1093/intqhc/mzm042.

21. Markham S. Patient perspective on predictive models in healthcare: translation into practice, ethical implications and limitations? BMJ Health Care Inform. 2025;32(1):e101153. doi:10.1136/bmjhci-2024-101153.

22. Goldschmitt M, Gleim P, Mandelartz S, Kellmeyer P, Rigotti T. Digitalizing informed consent in healthcare: a scoping review. BMC Health Serv Res. 2025;25:893. doi:10.1186/s12913-025-12964-7.

23. Khan MM, Shah N, Shaikh N, Thabet A, Alrabayah T, Belkhair S. Towards secure and trusted AI in healthcare: a systematic review of emerging innovations and ethical challenges. Int J Med Inform. 2025;195:105780. doi:10.1016/j.ijmedinf.2024.105780.

24. Penner SB, Mercado NR, Bernstein S, Erickson E, DuBois MA, Dreisbach C. Fostering informed consent and shared decision-making in maternity nursing with the advancement of artificial intelligence. MCN Am J Matern Child Nurs. 2025;50(2):78–85. doi:10.1097/NMC.0000000000001083.

25. Venerito V, Gupta L, Mileto S, Iannone F, Bilgin E. Ethical challenges and regulatory pathways for artificial intelligence in rheumatology. Rheumatol Adv Pract. 2025;9(2):rkaf035. doi:10.1093/rap/rkaf035.

26. Campos H, Salmi L. Critical AI health literacy as liberation technology: a new skill for patient empowerment. NAM Perspect. 2025. doi:10.31478/202512a.