Explainable artificial intelligence in pancreatic cancer prediction: from transparency to clinical decision-making
XAI has the potential to serve as a cornerstone for advancing transparent, trustworthy ML in PC prediction. By clarifying model reasoning, XAI enhances clinical interpretability and regulatory readiness. This review provides a technical and clinical synthesis of current XAI practices, positioning explainability as essential for translating ML innovations into actionable oncology tools.
Authors
- Asma Abdullah Alfayez (ORCID: https://orcid.org/0000-0002-7448-2095)
- Wardah Alharbi (ORCID: https://orcid.org/0000-0002-9912-4622)
Institutions
- King Saud bin Abdulaziz University for Health Sciences (SA)
- King Abdullah International Medical Research Center (SA)
- National Guard Health Affairs (SA)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-12-17
- DOI
- https://doi.org/10.3389/fonc.2025.1720039
- Primary Topic
- Pancreatic and Hepatic Oncology Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- King Abdullah International Medical Research Center