Acceptance of AI-based clinical decision support systems

The use of artificial intelligence (AI) can significantly improve diagnostic reliability in medicine. Why do many patients and doctors still reject the use of AI in diagnosis?

Factsheet

  • Schools involved Business School
  • Institute(s) Institute for Marketing & Global Management
  • Research unit(s) Marketing
  • Strategic thematic field Thematic field "Humane Digital Transformation"
  • Funding organisation BFH
  • Duration (planned) 01.01.2024 - 31.12.2024
  • Head of project Prof. Dr. Elisa Konya-Baumbach
  • Project staff Prof. Dr. Elisa Konya-Baumbach
    Prof. Dr. Gert Krummrey
  • Keywords Artificial Intelligence, AI, Psychology of AI Acceptance, Clinical Decision Support Systems, CDSS, Medical AI

Situation

In medicine, a correct diagnosis is a basic prerequisite for targeted therapy. Diagnostic decision-making is a complex process that integrates symptoms, clinical findings, and the results of instrumental examinations. Diagnostic errors affect up to 15% of all emergency patients. Doctors are already supported by applications when making a diagnosis, but these mostly follow simple decision trees. The use of AI can significantly improve diagnostic reliability. While the acceptance of AI is crucial to exploit its potential, many patients and doctors still reject the use of AI in a medical context. The aim of this project is to investigate the perception and acceptance of patients and doctors regarding the use of AI in diagnosis and to develop measures to increase acceptance and use.

Course of action

The research questions are answered using a mixed-method approach in order to qualitatively identify acceptance barriers and quantitatively validate them via experimental studies.

Result

The research on the use of AI, particularly in the sensitive medical context, aims to contribute to medical and technological progress for society as a whole. The project contributes to the human digital transformation by putting the human in the spotlight in order to realize medical-technological progress.

This project contributes to the following SDGs

  • 3: Good health and well-being
  • 9: Industry, innovation and infrastructure