Abstract
BACKGROUND: Digitalization is currently revolutionizing health care worldwide. A promising technology in this context is artificial intelligence (AI). The application of AI can support health care providers in their daily work in various ways. The integration of AI is particularly promising in clinical decision support systems (CDSSs). While the opportunities of this technology are numerous, the problems should not be overlooked. OBJECTIVE: This study aimed to identify challenges and barriers in the context of AI-based CDSSs from the perspectives of experts across various disciplines. METHODS: Semistructured expert interviews were conducted with different stakeholders. These included representatives of patients, physicians and caregivers, developers of AI-based CDSSs, researchers (studying AI in health care and social and health law), quality management and quality assurance representatives, a representative of an ethics committee, a representative of a health insurance fund, and medical product consultants. The interviews took place on the web and were recorded, transcribed, and subsequently subjected to a qualitative content analysis based on the method by Kuckartz. The analysis was conducted using MAXQDA software. Initially, the problems were separated into "general," "development," and "clinical use." Finally, a workshop within the project consortium served to systematize the identified problems. RESULTS: A total of 15 expert interviews were conducted, and 309 expert statements with reference to problems and barriers in the context of AI-based CDSSs were identified. These emerged in 7 problem categories: technology (46/309, 14.9%), data (59/309, 19.1%), user (102/309, 33%), studies (17/309, 5.5%), ethics (20/309, 6.5%), law (33/309, 10.7%), and general (32/309, 10.4%). The problem categories were further divided into problem areas, which in turn comprised the respective problems. CONCLUSIONS: A large number of problems and barriers were identified in the context of AI-based CDSSs. These can be systematized according to the point at which they occur ("general," "development," and "clinical use") or according to the problem category ("technology," "data," "user," "studies," "ethics," "law," and "general"). The problems identified in this work should be further investigated. They can be used as a basis for deriving solutions to optimize development, acceptance, and use of AI-based CDSSs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/preprints.62704.