Explainable artificial intelligence in pancreatic cancer prediction: from transparency to clinical decision-making.

ABSTRACT:

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

SUBMITTER: Alharbi W 

PROVIDER: S-EPMC12753400 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature