The I-Kapcam-Ai-Q

A novel instrument for evaluating health care providers’ AI awareness in Italy

Bibliographic Data

ID22073304
AuthorsVincenza Cofini (0000-0003-1107-418X, University of L'Aquila), Laura Piccardi (0000-0002-1322-5538, Università degli studi di Cassino e del Lazio Meridionale), Eugenio Benvenuti (University of L'Aquila), Ginevra Di Pangrazio (University of L'Aquila), Eleonora Cimino (0009-0000-5955-4022, University of L'Aquila), Martina Mancinelli (Sapienza University of Rome), Mario Muselli (0000-0001-5816-2393, University of L'Aquila, corresponding author), Emiliano Petrucci (San Salvatore Hospital), Giovanna Picchi (0000-0002-2036-9580, University of L'Aquila), Patrizia Palermo (0000-0002-6095-635X, University of L'Aquila), L Tobia (0000-0003-1532-932X, University of L'Aquila), Arcangelo Barbonetti (0000-0002-6888-9585, University of L'Aquila), Giovambattista Desideri (0000-0002-0145-1271, Sapienza University of Rome), Maurizio Guido (0000-0001-6554-0874, University of L'Aquila), Franco Marinangeli (0000-0002-4931-7717, University of L'Aquila), Leila Fabiani (0000-0002-0597-1450, University of L'Aquila), Stefano Necozione (0000-0003-2501-1665, University of L'Aquila)
Year2025
Volume13
Pages1655659-1655659
Publication date2025-09-18
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2025.1655659
PMID41048310
OpenAlexW4414306169
LanguageEN
References cited41

Background Understanding healthcare providers’ readiness and attitudes is crucial for integrating AI in healthcare, yet no validated tool exists to evaluate these aspects among Italian physicians. This study developed and validated the Italian Knowledge, Attitudes, Practice, and Clinical Agreement between Medical Doctors and the Artificial Intelligence Questionnaire (I-KAPCAM-AI-Q). Methods This was a cross-sectional validation study. The validation process included expert review ( n = 18), face validity assessment ( n = 20), technical implementation testing, and pilot testing ( n = 203) with both residents and specialists. The questionnaire contained 29 items, one clinical universal scenario, and 6 clinical scenarios specific to 6 specialists. Results The questionnaire demonstrated strong content validity (S-CVI/Ave = 0.98) and acceptable internal consistency (Cronbach’s Alpha = 0.7481, KR-21 = 0.832). Pilot testing revealed only 17% of participants had received digital technology training during medical education, while 91% showed clinical agreement with AI-proposed diagnoses. Knowledge in diagnostics was highest among AI applications (48%). Residents showed higher interest in technical support (58.3% vs. 42.0%, p = 0.021) and evidence-based validation (61.2% vs. 47.0%, p = 0.043) compared to specialists. Conclusion The I-KAPCAM-AI-Q provides a reliable tool for assessing healthcare providers’ AI readiness and highlights the need for enhanced digital health education in medical curricula

Clinical decision support system · Digital health · Face validity · Health care · Internal consistency · Artificial Intelligence in Healthcare and Education · Clinical Reasoning and Diagnostic Skills · Electronic Health Records Systems

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