{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["11(2)"],"submitter":["Manz R"],"funding":["Augsburg University, Intramural Funding"],"pubmed_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"],"journal":["The journal of pathology. Clinical research"],"pagination":["e70023"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11904816"],"repository":["biostudies-literature"],"pubmed_title":["Do explainable AI (XAI) methods improve the acceptance of AI in clinical practice? An evaluation of XAI methods on Gleason grading."],"pmcid":["PMC11904816"],"pubmed_authors":["Hinske LC","Cramer S","Backer J","Wengenmayr C","Muzalyova A","Muller D","Huss R","Raffler J","Soto-Rey I","Manz R","Rentschler L","Meyer P"],"additional_accession":[]},"is_claimable":false,"name":"Do explainable AI (XAI) methods improve the acceptance of AI in clinical practice? An evaluation of XAI methods on Gleason grading.","description":"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","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Mar","modification":"2025-04-04T00:32:12.109Z","creation":"2025-04-04T00:32:12.109Z"},"accession":"S-EPMC11904816","cross_references":{"pubmed":["40079401"],"doi":["10.1002/2056-4538.70023"]}}