{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["9"],"submitter":["Zamir MT"],"pubmed_abstract":["<h4>Background</h4>Artificial intelligence (AI) is increasingly being integrated into clinical diagnostics; yet, its lack of transparency hinders trust and adoption among health care professionals. The explainable artificial intelligence (XAI) has the potential to improve the interpretability and reliability of AI-based decisions in clinical practice.<h4>Objective</h4>This study evaluates the use of XAI for interpreting radiology reports to improve health care practitioners' confidence and comprehension of AI-assisted diagnostics.<h4>Methods</h4>This study used the Indiana University chest x-ray dataset containing 3169 textual reports and 6471 images. Textual data were being classified as either normal or abnormal by using a range of machine learning approaches. This includes traditional m"],"journal":["JMIR formative research"],"pagination":["e77482"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12569488"],"repository":["biostudies-literature"],"pubmed_title":["Explainable AI-Driven Analysis of Radiology Reports Using Text and Image Data: Experimental Study."],"pmcid":["PMC12569488"],"pubmed_authors":["Gelbukh I","Zamir MT","Felipe Riveron EM","Khan SU","Gelbukh A"],"additional_accession":[]},"is_claimable":false,"name":"Explainable AI-Driven Analysis of Radiology Reports Using Text and Image Data: Experimental Study.","description":"<h4>Background</h4>Artificial intelligence (AI) is increasingly being integrated into clinical diagnostics; yet, its lack of transparency hinders trust and adoption among health care professionals. The explainable artificial intelligence (XAI) has the potential to improve the interpretability and reliability of AI-based decisions in clinical practice.<h4>Objective</h4>This study evaluates the use of XAI for interpreting radiology reports to improve health care practitioners' confidence and comprehension of AI-assisted diagnostics.<h4>Methods</h4>This study used the Indiana University chest x-ray dataset containing 3169 textual reports and 6471 images. Textual data were being classified as either normal or abnormal by using a range of machine learning approaches. This includes traditional m","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Oct","modification":"2026-06-05T06:57:45.752Z","creation":"2026-06-04T03:06:00.263Z"},"accession":"S-EPMC12569488","cross_references":{"pubmed":["40997754"],"doi":["10.2196/77482"]}}