Conditional Acceptance and the Optimism–Knowledge Gap: A Scoping Review of Attitudes and Perceptions of Artificial Intelligence in Healthcare in Italy

Background/Objectives: Artificial intelligence (AI) is integrated into diagnostic, thera-peutic, administrative, and communicative healthcare domains in Italy under regulations requiring human oversight. Empirical evidence on AI attitudes, acceptance, and per-ceptions in Italian healthcare is rapidly accumulating but not systematically mapped. This scoping review aimed to (i) map empirical evidence on AI attitudes, acceptance, and perceptions in Italy by population and domain; (ii) identify measurement instruments used in studies and their origins; and (iii) characterize determinants, themes, and methodological gaps in the Italian evidence base. Methods: The review used Joanna Briggs Institute methodology, reported via PRISMA-ScR (protocol Open Science Framework doi: 10.17605/OSF.IO/TZRVF). PubMed and Embase were searched on 27 April 2026 from January 2018 in English, Italian, or German, combining controlled vo-cabulary and free-text terms across AI, attitudes-acceptance, and healthcare delivery, with an Italian-context qualifier. Eligibility criteria used the Population–Concept–Context mnemonic. Results: Of 1,510 unique records screened, 35 empirical studies were retained, comprising seven studies of Italian patients and the general population, 22 studies of healthcare professionals, three psychometric validation studies of AI-acceptance instru-ments, one mixed-population study and two international comparator studies with sub-stantial Italian sub-samples. Acceptance was consistently positive but conditional on physician oversight, training and regulatory clarity. A recurrent optimism–knowledge gap and an absence of probabilistic, population-representative evidence were identified as principal gaps. Conclusions: Italian evidence on AI attitudes is expanding but methodologically narrow. Three Italian-validated acceptance instruments are now available. Population-representative, multilingual and longitudinal evidence is required.