JMIR Publications

ABSTRACT

Background:

Digitalization is currently revolutionizing healthcare all over the world. A promising technology in this context is artificial intelligence (AI). The application of AI can support healthcare providers in their daily work in various ways. The integration of AI is particularly promising in clinical decision support systems (CDSS). While the opportunities of this technology are numerous, the problems should not be overlooked.

Objective:

This study aims to identify challenges and barriers in the context of AI-based CDSS from the perspectives of experts across various disciplines.

Methods:

Semi-structured expert interviews were conducted with different stakeholders. These included representatives of patients, physicians and caregivers, developers of AI-based CDSS, researchers (AI in healthcare / social and health law), representatives of quality management and quality assurance, a representative of an ethics committee, a representative of health insurance funds, and medical product consultants. The interviews took place web-based, were recorded, transcribed and subsequently subjected to a qualitative content analysis based on Kuckartz. The analysis was conducted using the software MAXQDA. Initially, the problems were separated into 1) General, 2) Development, and 3) Clinical Use. Finally, a workshop within the consortium served to systematize the identified problems.

Results:

Fifteen expert interviews were conducted. In total, 309 expert statements with reference to problems and barriers in the context of AI-based CDSS were identified. These emerged in seven problem categories: Technology (n=46), Data (n=59), User (n=102), Studies (n=17), Ethics (n=20), Law (n=33), and General (n=32). The problem categories were further divided into problem areas. These included the respective problems.

Conclusions:

A large number of problems and barriers were identified in the context of AI-based CDSS. These can be systematized according to the point in time 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 within 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 CDSS.