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Review Open access

The Role of Artificial Intelligence in the Management of Pancreatic Cancer: Current Evidence and Future Perspectives

Jul 2026 · Onco · Vol 6, pp. 33 · 0 citations · 87 references

TL;DR

Artificial intelligence has significant potential to enhance the management of pancreatic cancer, particularly as a decision-support tool, however, further prospective validation and integration into clinical workflows are required before widespread adoption.

Abstract

(1) Background: Pancreatic ductal adenocarcinoma remains one of the most lethal malignancies due to late diagnosis, aggressive tumor biology, and limited therapeutic options. Artificial intelligence has emerged as a promising tool to improve detection, risk stratification, and treatment planning. This study aims to review the current clinical applications of artificial intelligence in the management of pancreatic cancer and evaluate its translational potential. (2) Methods: A comprehensive literature search was conducted across major databases (PubMed/MEDLINE, Scopus, Web of science and Cochrane) for studies published between 2015 and 2026. Eligible studies included clinical investigations and systematic reviews reporting quantifiable outcomes related to diagnosis, staging, prognostication, and treatment response using artificial intelligence methods. (3) Results: A total of 24 studies were included, most of which were retrospective and utilized imaging, histopathology, and clinical datasets. Artificial intelligence demonstrated high diagnostic performance, particularly in imaging-based detection and lesion characterization, with several models achieving excellent accuracy. Applications in staging, surgical planning, and prognostication also showed promising results, although external validation and prospective data were limited. (4) Conclusions: Artificial intelligence has significant potential to enhance the management of pancreatic cancer, particularly as a decision-support tool. However, further prospective validation and integration into clinical workflows are required before widespread adoption.

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