Explainable AI-Driven Analysis of Radiology Reports Using Text and Image Data: Experimental Study.

ABSTRACT:

Background

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.

Objective

This study evaluates the use of XAI for interpreting radiology reports to improve health care practitioners' confidence and comprehension of AI-assisted diagnostics.

Methods

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

SUBMITTER: Zamir MT 

PROVIDER: S-EPMC12569488 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature