TRUST, PRIVACY, AND LIABILITY: EXPLORING PATIENT ATTITUDES TOWARD ARTIFICIAL INTELLIGENCE IN HEALTHCARE

Authors

DOI:

https://doi.org/10.31435/ijitss.3(51).2026.6315

Keywords:

Artificial Intelligence, Patient Perspectives, Healthcare Digitalization, Medical Ethics, Trust in AI

Abstract

Background: The aim of this study was to evaluate the level of trust and social acceptance regarding the use of artificial intelligence (AI) in medical self-diagnosis, and to identify patients' primary concerns related to the implementation of AI algorithms in the healthcare system.

Materials and Methods: The research was carried out in July 2026 via a cross-sectional survey design utilizing an anonymous online questionnaire (the CAWI technique). The study cohort comprised 274 participants. The analysis evaluated digital health information-seeking behaviors, the acceptance of artificial intelligence in clinical and diagnostic workflows, and patients' perceptions of responsibility for medical errors caused by AI systems.

Results: Nearly all participants (96%) reported verifying their medical symptoms online. Patients demonstrated moderate acceptance of AI as a supportive tool in analyzing medical imaging; however, they strongly rejected (80% of respondents) the concept of fully automated treatment processes without physician supervision. Significant concerns regarding the privacy of medical data were identified, particularly within the 36–50 age demographic. The analysis of liability for AI-generated errors revealed that patients tend to attribute responsibility to themselves, highlighting a discrepancy between their moral intuition and current legal and ethical frameworks.

Conclusions: Public acceptance of AI in healthcare is strictly dependent on preserving the "human-in-the-loop" paradigm. There is a critical need to enhance health education regarding cybersecurity and to establish clear regulatory frameworks for AI-related errors, all while maintaining the physician's overriding authority in the therapeutic process.

References

Thirunavukarasu, A. J., Ting, D. S. J., Elangovan, K., Gutierrez, L., Tan, T. F., & Ting, D. S. W. (2023). Large language models in medicine. Nature Medicine, 29(8), 1930–1940. https://doi.org/10.1038/s41591-023-02448-8

Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., & Tseng, V. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digital Health, 2(2), e0000198. https://doi.org/10.1371/journal.pdig.0000198

Topol E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7

Starcevic, V., & Berle, D. (2013). Cyberchondria: Towards a better understanding of excessive health-related Internet use. Expert Review of Neurotherapeutics, 13(2), 205–213. https://doi.org/10.1586/ern.12.162

Cocco, A. M., Zordan, R., Taylor, D. M., Weiland, T. J., Dilley, S. J., Kant, J., Dombagolla, M., Hendarto, A., Lai, F., & Hutton, J. (2018). Dr Google in the ED: Searching for online health information by adult emergency department patients. The Medical Journal of Australia, 209(8), 342–347. https://doi.org/10.5694/mja17.00889

Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y. S., & Coiera, E. (2018). Conversational agents in healthcare: A systematic review. Journal of The American Medical Informatics Association : JAMIA, 25(9), 1248–1258. https://doi.org/10.1093/jamia/ocy072

Nadarzynski, T., Miles, O., Cowie, A., & Ridge, D. (2019). Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study. Digital Health, 5, 2055207619871808. https://doi.org/10.1177/2055207619871808

Richardson, J.P., Smith, C., Curtis, S., Watson, S., Zhu, X., & Zhu, X. (2021). Patient apprehensions about the use of artificial intelligence in healthcare. npj Digital Medicine 4, 140. https://doi.org/10.1038/s41746-021-00509-1

World Health Organization (2021). Ethics and governance of artificial intelligence for health: WHO guidance. World Health Organization. https://iris.who.int/handle/10665/341996

Lupton, D., & Jutel, A. (2015). 'It's like having a physician in your pocket!' A critical analysis of self-diagnosis smartphone apps. Social Science & Medicine (1982), 133, 128–135. https://doi.org/10.1016/j.socscimed.2015.04.004

Lennartz, S., Dratsch, T., Zopfs, D., Persigehl, T., Maintz, D., Große Hokamp, N., & Pinto Dos Santos, D. (2021). Use and control of artificial tntelligence in patients across the medical workflow: Single-center questionnaire study of patient perspectives. Journal of Medical Internet Research, 23(2), e24221. https://doi.org/10.2196/24221

Moy, S., Irannejad, M., Manning, S. J., Farahani, M., Ahmed, Y., Gao, E., Prabhune, R., Lorenz, S., Mirza, R., & Klinger, C. (2024). Patient perspectives on the use of artificial intelligence in health care: A scoping review. Journal of Patient-Centered Research and Reviews, 11(1), 51–62. https://doi.org/10.17294/2330-0698.2029

Amann, J., Blasimme, A., Vayena, E., Frey, D., Madai, V. I., & Precise4Q consortium (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1), 310. https://doi.org/10.1186/s12911-020-01332-6

He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019). The practical implementation of artificial intelligence technologies in medicine. Nature Medicine, 25(1), 30–36. https://doi.org/10.1038/s41591-018-0307-0

Esmaeilzadeh P. (2020). Use of AI-based tools for healthcare purposes: a survey study from consumers' perspectives. BMC Medical Informatics and Decision Making, 20(1), 170. https://doi.org/10.1186/s12911-020-01191-1

Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1), 195. https://doi.org/10.1186/s12916-019-1426-2

Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLoS Medicine, 15(11), e1002689. https://doi.org/10.1371/journal.pmed.1002689

Grote, T., & Berens, P. (2020). On the ethics of algorithmic decision-making in healthcare. Journal of Medical Ethics, 46(3), 205–211. https://doi.org/10.1136/medethics-2019-105586

Downloads

Published

2026-09-17

How to Cite

Głodek, K., Kamińska, M., & Duda, A. (2026). TRUST, PRIVACY, AND LIABILITY: EXPLORING PATIENT ATTITUDES TOWARD ARTIFICIAL INTELLIGENCE IN HEALTHCARE. International Journal of Innovative Technologies in Social Science, 4(3(51). https://doi.org/10.31435/ijitss.3(51).2026.6315

Most read articles by the same author(s)