Explainable AI-Driven Analysis of Radiology Reports Using Text and Image Data: Experimental Study
The research underscores that explainability is an essential component of any AI systems used in diagnostics and is helpful in the design and implementation of AI in the health care sector. Such an approach improves the accuracy of the diagnosis and builds confidence in health workers, who in the future will use XAI in clinical settings, particularly in the application of AI explainability for medical purposes.
Authors
- Edgardo M. Felipe‐Riverón (ORCID: https://orcid.org/0000-0002-9828-3568)
- Alexander Gelbukh (ORCID: https://orcid.org/0000-0001-7845-9039)
- Irina Gelbukh (ORCID: https://orcid.org/0000-0001-7295-5752)
- Muhammad Tayyab Zamir
- Safir Ullah Khan (ORCID: https://orcid.org/0000-0002-1285-8907)
Institutions
- Tecnológico Nacional de México (MX)
- National University (SD)
- Instituto Politécnico Nacional (MX)
Publication Details
- Journal
- JMIR Formative Research
- Published
- 2025-09-25
- DOI
- https://doi.org/10.2196/77482
- Citations
- 2
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 1.61