Patient Confidence in Artificial Intelligence-Assisted Imaging Reports for Early Cancer Detection

Bi Chao; Dewi Asih; Ikhwanul Hakim Nasution

doi:10.61919/aa2r3f36

Authors

Bi Chao Imaging Department, Universitas Prima Indonesia, Indonesia Author
Dewi Asih Imaging Department, Universitas Prima Indonesia, Indonesia Author
Ikhwanul Hakim Nasution Imaging Department, Universitas Prima Indonesia, Indonesia Author

DOI:

https://doi.org/10.61919/aa2r3f36

Keywords:

artificial intelligence; AI-assisted imaging; early cancer detection; patient confidence; medical imaging; radiology; patient-centered care

Abstract

Background: Artificial intelligence is increasingly used to support medical imaging, including cancer detection workflows. Although technical performance is important, patient confidence in AI-assisted imaging depends on how patients understand AI’s role, whether clinicians remain responsible, and whether concerns about error, privacy, bias, and accountability are addressed. Qualitative evidence is needed to explore how patients interpret AI-supported imaging reports in emotionally sensitive cancer-related contexts. Objective: This study explored how adults with prior imaging exposure or cancer screening familiarity perceived AI-assisted imaging reports for early cancer detection, what increased or reduced their confidence, and what safeguards they considered necessary for acceptable use. Methods: An interpretivist qualitative study was conducted using semi-structured interviews with eight adult participants. Participants had experience of medical imaging or familiarity with cancer screening-related services. Interview data were analyzed using reflexive thematic analysis to identify patterns in perceived usefulness, concerns, trust conditions, and communication needs. Results: Eight themes were identified: AI as a supportive imaging tool, limited awareness and lack of disclosure, conditional confidence through human supervision, trust through reliability and approval, AI as a facilitator of earlier detection, fear of mistakes and overdiagnosis, ethical and social concerns, and assurance through clear communication. Participants viewed AI as useful when it acted as a second reader, but confidence depended on clinician verification, transparent disclosure, privacy protection, accountability, and simple explanations of AI’s role and follow-up plans. Conclusion: Patient confidence in AI-assisted cancer imaging is conditional rather than automatic. AI may be acceptable when embedded in clinician-led care, supported by transparent communication, reliable governance, privacy safeguards, and clear responsibility for final interpretation.

References

1. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229-63. doi:10.3322/caac.21834.

2. World Health Organization. Guide to cancer early diagnosis. Geneva: World Health Organization; 2017. Available from: https://www.who.int/publications/i/item/9789241511940. Cited 2026 Jun 14.

3. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500-10. doi:10.1038/s41568-018-0016-5.

4. European Society of Radiology. What the radiologist should know about artificial intelligence: an ESR white paper. Insights Imaging. 2019;10(1):44. doi:10.1186/s13244-019-0738-2.

5. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94. doi:10.1038/s41586-019-1799-6.

6. Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019;25(6):954-61. doi:10.1038/s41591-019-0447-x.

7. Lång K, Josefsson V, Larsson AM, Larsson S, Högberg C, Sartor H, et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial. Lancet Oncol. 2023;24(8):936-44. doi:10.1016/S1470-2045(23)00298-X.

8. Young AT, Amara D, Bhattacharya A, Wei ML. Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review. Lancet Digit Health. 2021;3(9):e599-611. doi:10.1016/S2589-7500(21)00132-1.

9. Lennartz S, Dratsch T, Zopfs D, Persigehl T, Maintz D, Hokamp NG, et al. Use and control of artificial intelligence in patients across the medical workflow: single-center questionnaire study of patient perspectives. J Med Internet Res. 2021;23(2):e24221. doi:10.2196/24221.

10. Richardson JP, Smith C, Curtis S, Watson S, Zhu X, Barry B, et al. Patient apprehensions about the use of artificial intelligence in healthcare. NPJ Digit Med. 2021;4(1):140. doi:10.1038/s41746-021-00509-1.

11. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195. doi:10.1186/s12916-019-1426-2.

12. Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology. 2018;286(3):800-9. doi:10.1148/radiol.2017171920.

13. Chen IY, Joshi S, Ghassemi M. Treating health disparities with artificial intelligence. Nat Med. 2020;26(1):16-7. doi:10.1038/s41591-020-0827-3.

14. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-53. doi:10.1126/science.aax2342.

15. National Cancer Institute. Cancer screening overview (PDQ): health professional version. Bethesda: National Cancer Institute; 2023. Available from: https://www.cancer.gov/about-cancer/screening/hp-screening-overview-pdq. Cited 2026 Jun 14.

16. Ongena YP, Yakar D, Haan M, Kwee TC. Patients' views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire. Eur Radiol. 2020;30(2):1033-40. doi:10.1007/s00330-019-06486-0.

17. Baghdadi LR, Mobeirek AA, Alhudaithi DR, Alqahtani HA, Alotaibi H, Alqahtani A, et al. Patients' attitudes toward the use of artificial intelligence as a diagnostic tool in radiology in Saudi Arabia: cross-sectional study. JMIR Hum Factors. 2024;11:e53108. doi:10.2196/53108.

18. Hoff KA, Bashir M. Trust in automation: integrating empirical evidence on factors that influence trust. Hum Factors. 2015;57(3):407-34. doi:10.1177/0018720814547570.

19. Glikson E, Woolley AW. Human trust in artificial intelligence: review of empirical research. Acad Manag Ann. 2020;14(2):627-60. doi:10.5465/annals.2018.0057.

20. U.S. Food and Drug Administration. Artificial intelligence and machine learning in software as a medical device. Silver Spring: U.S. Food and Drug Administration; 2024. Available from: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-software-medical-device. Cited 2026 Jun 14.

21. World Health Organization. Ethics and governance of artificial intelligence for health. Geneva: World Health Organization; 2021. Available from: https://www.who.int/publications/i/item/9789240029200. Cited 2026 Jun 14.

22. Gerke S, Minssen T, Cohen IG. Ethical and legal challenges of artificial intelligence-driven healthcare. In: Bohr A, Memarzadeh K, editors. Artificial intelligence in healthcare. London: Academic Press; 2020. p. 295-336. doi:10.1016/B978-0-12-818438-7.00012-5.

23. 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):e745-50. doi:10.1016/S2589-7500(21)00208-9.

24. Jaremko JL, Azar M, Bromwich R, Lum A, Alicia Cheong LH, Gibert M, et al. Canadian Association of Radiologists white paper on ethical and legal issues related to artificial intelligence in radiology. Can Assoc Radiol J. 2019;70(2):107-18. doi:10.1016/j.carj.2019.03.001.

25. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13(3):319-40. doi:10.2307/249008.

26. Venkatesh V, Thong JYL, Xu X. Unified theory of acceptance and use of technology: a synthesis and the road ahead. J Assoc Inf Syst. 2016;17(5):328-76. doi:10.17705/1jais.00428.

27. Creswell JW, Poth CN. Qualitative inquiry and research design: choosing among five approaches. 4th ed. Thousand Oaks: Sage Publications; 2018.

28. Kallio H, Pietilä AM, Johnson M, Kangasniemi M. Systematic methodological review: developing a framework for a qualitative semi-structured interview guide. J Adv Nurs. 2016;72(12):2954-65. doi:10.1111/jan.13031.

29. Braun V, Clarke V. Reflecting on reflexive thematic analysis. Qual Res Sport Exerc Health. 2019;11(4):589-97. doi:10.1080/2159676X.2019.1628806.

30. Nowell LS, Norris JM, White DE, Moules NJ. Thematic analysis: striving to meet the trustworthiness criteria. Int J Qual Methods. 2017;16(1):1609406917733847. doi:10.1177/1609406917733847.