Explainable artificial intelligence in pancreatic cancer prediction: from transparency to clinical decision-making
Wardah Alharbi, Asma Abdullah Alfayez · 2025
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Description
Pancreatic cancer kills most patients within five years, and finding it early is the best lever doctors have. Wardah Alharbi and Asma Abdullah Alfayez review how machine learning is being taught to show its work in this disease. Their survey covers studies from 2020 to 2025 that pair prediction models with explainability tools.
Of twenty-one learning studies, only three truly built explanation methods in, mostly a technique called SHAP. Those methods surfaced the markers and health conditions driving each prediction and raised clinician trust. The review sorts explanation styles by when they act, what models they fit, and how wide they look. Adoption is held back by shaky methods, little outside testing, and poor fit to hospital routines. The authors call explainability essential, not decorative.
Catalog record
| CREATOR | Wardah Alharbi, Asma Abdullah Alfayez |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 40 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |