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FrancoAngeli Journals

Materiali per la ricerca e l'approfondimento

No. 135 (2025)

Artificial Intelligence in Italian Public Health: Perceptions and Expectations of Italian Citizens

DOI
https://doi.org/10.3280/mesa2025-135oa23431
Submitted
agosto 3, 2026
Published
2026-10-01

Abstract

Background. The widespread adoption of artificial intelligence (AI) systems in the healthcare sector is one of the main drivers of transformation within the National Health Service (SSN). Despite growing investment in digital health and the gradual evolution of the regulatory framework — from the European AI Act to Law 132/2025 — the Italian literature on public perceptions of AI in healthcare remains fragmented and primarily focused on clinical practitioners rather than citizens.
Objectives. This article presents and discusses the results of a national public opinion survey aimed at gauging the perceptions, expectations, and concerns of the Italian adult population regarding the adoption of AI in the NHS, with
particular attention to the dimensions of perceived utility, institutional trust, and the role of the medical profession.
Methods. The survey was conducted by the DEMOPOLIS Institute on behalf of FIASO among a sample of 3,400 adults, statistically representative of the Italian adult population by gender, age, and geographic area. The methodology integrated CAWI, CATI, and CAMI techniques with a preliminary phase of open-ended qualitative interviews. Data were collected in 2025.
Results. 61% of respondents expect AI to improve health management and disease treatment over the next decade.
63% consider AI in healthcare to be a sustainable innovation. The perceived benefits focus on the analysis of clinical data (73%), the reduction of bureaucratic burdens (61%), and improved access to services (58%). At the same time, 85% of respondents believe that AI can complement but not replace medical assessment, indicating a pattern of conditional acceptance consistent with theoretical models of technology acceptance extended to the trust dimension.
Conclusions. The results suggest that Italian citizens show a greater willingness to adopt AI in healthcare than the public debate would suggest, but this is contingent on specific conditions: human oversight, algorithmic transparency, and guaranteed equity in access.
The managerial implications for healthcare organizations concern innovation governance, institutional communication, and the design of citizen-centered adoption pathways.

References

  1. Anitec-Assinform (2025). Tecnologie e policy per il futuro della sanità digitale (White Paper). Roma: Anitec-Assinform/NetConsulting cube.
  2. Anitec-Assinform (2026). Il Digitale in Italia 2026. Roma: Anitec-Assinform/NetConsulting cube.
  3. Ardito V., Cappellaro G., Compagni A., Petracca F., Preti L.M. (2025). Adoption of artificial intelligence applications in clinical practice: Insights from a Survey of Healthcare Organizations in Lombardy, Italy. Digital Health, Jul 10, 11, 20552076251355680.
  4. DOI: 10.1177/20552076251355680.
  5. Baldus S.G., Wiesmann M., Habel U., Gerhards A., Hasan D., Weyland C.S., Truhn D., Hasl M.M., Clemens B., Nikoubashman O. (2026). Patients’ views on the use of artificial intelligence in healthcare: Artificial Intelligence Survey Aachen (AISA) – a prospective survey. Insights Imaging, Jan 5, 17(1): 6. DOI: 10.1186/s13244-025-02159-3.
  6. Bowling A. (2014). Research Methods in Health: Investigating Health and Health Services. Maidenhead: Open University Press.
  7. Bracic A., Spector-Bagdady K., Towle S., Zhang R., James C.A., Price W.N. (2026). Factors for Patient Trust and Acceptance of Medical Artificial Intelligence. JAMA Network Open, Mar 2, 9(3), e260815. DOI: 10.1001/jamanetworkopen.2026.0815.
  8. Cicin F.N., Çetin Gürkan G. (2026). How physicians embrace AI: insights from technology acceptance and trust theories. Frontiers in Digital Health, 8, 1722087. DOI: 10.3389/fdgth.2026.1722087.
  9. Corea F., Fossa F., Loreggia A. et al. (2023). A principle-based approach to AI: the case for European Union and Italy. AI & Soc, 38: 521-535. DOI: 10.1007/s00146-022-01453-8.
  10. Davis F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3): 319-339. DOI: 10.2307/249008.
  11. Isaksen A.A., Schaarup J.R., Bjerg L. et al. (2025). Changes in public perception of artificial intelligence in healthcare after exposure to ChatGPT. npj Digital Medicine, 8, 795. DOI: 10.1038/s41746-025-02169-x.
  12. Istituto DEMOPOLIS (2025). L’intelligenza artificiale e la salute: indagine demoscopica nazionale per FIASO. Roma: Istituto DEMOPOLIS.
  13. JRC – Joint Research Centre (2024). Public perception of socially assistive robots for healthcare in the EU: A large-scale survey. Computers in human behavior reports, 15, 100465, JRC132947. DOI: 10.1016/j.chbr.2024.100465 (online).
  14. Ministero della Salute (2024). PNRR – Missione 6 Salute. Roma, Ministero della Salute.
  15. Ministero della Salute – AgID (2024). Piano Triennale per la Sanità Digitale 2024-2026. Roma.
  16. Nong P., Platt J. (2025). Patients’ Trust in Health Systems to Use Artificial Intelligence. JAMA Network Open, 8(2), e2460628. DOI: 10.1001/jamanetworkopen.2024.60628.
  17. Osservatorio Sanità Digitale – Politecnico di Milano (2026). Consolidare il futuro: la Sanità tra investimenti da valorizzare e nuove sfide dell’AI. Milano: POLIMI School of Management, maggio 2026.
  18. Parlamento Europeo (2024). Artificial Intelligence Act (AI Act), Regolamento UE 2024/1689.
  19. Reis M., Reis F., Kunde W. (2025). Public Perception of Physicians Who Use Artificial Intelligence. JAMA Network Open, Jul 1, 8(7), e2521643. DOI: 10.1001/jamanetworkopen.2025.21643.
  20. Venkatesh V., Bala H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. Decision Sciences, 39(2): 273-315. DOI: 10.1111/j.1540-5915.2008.00192.x.
  21. Venkatesh V., Thong J.Y.L., Xu X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1): 157-178. DOI: 10.2307/41410412.
  22. Zondag A.G.M., Rozestraten R., Grimmelikhuijsen S., Jongsma K., van Solinge W., Bots M., Vernooij R., Haitjema S. (2024). The Effect of Artificial Intelligence on Patient-Physician Trust: Cross-Sectional Vignette Study. Journal of Medical Internet Research, 26, e50853. DOI: 10.2196/50853.