Conditional Acceptance and the Optimism-Knowledge Gap: A Scoping Review of Attitudes and Perceptions of Artificial...

Background/Objectives: Artificial intelligence (AI) is integrated into diagnostic, therapeutic, administrative, and communicative healthcare domains in Italy under regulations requiring human oversight. Empirical evidence on AI attitudes, acceptance, and perceptions 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: This 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 vocabulary and free-text terms across AI, attitudes-acceptance, and healthcare delivery, with an Italian-context qualifier; a complementary AI-assisted semantic search (Consensus Pro) was conducted to validate retrieval completeness. Eligibility criteria used the Population-Concept-Context mnemonic. Results: Of 1510 unique records screened, 35 empirical studies were retained, comprising 7 studies of Italian patients and the general population, 22 studies of healthcare professionals, 3 psychometric validation studies of AI-acceptance instruments, 1 mixed-population study, and 2 international comparator studies with substantial 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.