biostudies-literatureUnknown9Zamir MT<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 mJMIR formative researche77482https://www.ebi.ac.uk/biostudies/studies/S-EPMC12569488biostudies-literatureExplainable AI-Driven Analysis of Radiology Reports Using Text and Image Data: Experimental Study.PMC12569488Gelbukh IZamir MTFelipe Riveron EMKhan SUGelbukh AfalseExplainable AI-Driven Analysis of Radiology Reports Using Text and Image Data: Experimental Study.<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 m2025-01-01T00:00:00Z2025 Oct2026-06-05T06:57:45.752Z2026-06-04T03:06:00.263ZS-EPMC125694884099775410.2196/77482