Explainable artificial intelligence in pancreatic cancer prediction: from transparency to clinical decision-making
Open accessAbstract
Background/Objectives: Pancreatic cancer (PC) remains among the most lethal malignancies worldwide, with a persistently low 5-year survival rate despite advances in systemic therapies and surgical innovation. Machine learning (ML) has emerged as a transformative tool for early detection, prognostic modelling, and treatment planning in PC, yet widespread clinical use is constrained by the “black box” nature of many models. Explainable artificial intelligence (XAI) offers a pathway to reconcile model accuracy with clinical trust, enabling transparent, reproducible, and clinically meaningful predictions. Methods: We reviewed literature from 2020–2025, focusing on ML-based studies in PC that incorporated or discussed XAI techniques. Methods were grouped by model architecture, data modality, and interpretability framework. We synthesized findings to evaluate the technical underpinnings, interpretability outcomes, and clinical relevance of XAI applications. Results: Across 21 studies on ML in PC, only three studies explicitly integrated XAI, primarily using SHAP and SurvSHAP. These methods helped identify key biomarkers, comorbidities, and survival predictors, while enhancing clinician trust. XAI approaches were categorized by staging (ante-hoc vs. post-hoc ), compatibility (model-agnostic vs . model-specific), and scope (local vs . global explanations). Barriers to adoption included methodological instability, limited external validation, weak workflow integration, and lack of standardized evaluation. Conclusions: 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.
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In pancreatic cancer, only three of 21 machine learning studies explicitly integrated any XAI method.
“a review of 21 machine learning studies found that only three explicitly integrated any XAI method”
From A generative AI multi-agent framework with integrated XAI governance for cancer diagnostics: from multi-omics interpretation to lifestyle risk stratification (2026) · cites this paper as Alharbi and Alfayez, 2025
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“In pancreatic cancer, among the most lethal malignancies with a 5-year survival rate below 12%, a review of 21 machine learning studies found that only three explicitly integrated any XAI method (Alharbi and Alfayez, 2025).”
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- Authors
- Alharbi, W., & Alfayez, A. A.
- Affiliations
- King Saud bin Abdulaziz University for Health Sciences (SA)
- Corresponding author
- Alfayez, Asma Abdullah
- Venue
- Frontiers in Oncology
- Publisher
- Frontiers Media SA
- Published
- 2025-12-17
- Volume
- 15
- Pages
- 1720039-1720039
- ISSN
- 2234-943X (ISSN-L)
- PMC
- PMC12753400
- OpenAlex
- W4417415155
- Cited by
- 5
- References
- 51
- Citations by year
- 2026: 5
- FWCI
- 0.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Oncology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- King Abdullah International Medical Research Center
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Oncology › Pancreatic and Hepatic Oncology Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- explainable AI, pancreatic cancer, cancer prediction, SHAP, model interpretability, clinical decision-making, clinician trust, biomarker discovery, prognostic modelling, early detection, external validation
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