Do explainable AI (XAI) methods improve the acceptance of AI in clinical practice? An evaluation of XAI methods on Gleason grading.

ABSTRACT: This work aimed to evaluate both the usefulness and user acceptance of five gradient-based explainable artificial intelligence (XAI) methods in the use case of a prostate carcinoma clinical decision support system environment. In addition, we aimed to determine whether XAI helps to increase the acceptance of artificial intelligence (AI) and recommend a particular method for this use case. The evaluation was conducted on a tool developed in-house with different visualization approaches to the AI-generated Gleason grade and the corresponding XAI explanations on top of the original slide. The study was a heuristic evaluation of five XAI methods. The participants were 15 pathologists from the University Hospital of Augsburg with a wide range of experience in Gleason grading and AI. The evaluat

SUBMITTER: Manz R 

PROVIDER: S-EPMC11904816 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature