Artificial Intelligence for treatment outcomes in Pancreatic Cancer: a Scoping Review

Background Pancreatic cancer is among the most aggressive malignancies, characterized by poor survival outcomes. The potential of artificial intelligence (AI) to enhance prognostic predictions is increasingly recognized, as traditional tools have demonstrated limited accuracy. Objective This review aimed to evaluate the existing literature on the application of AI for predicting treatment outcomes in pancreatic cancer. The primary objective was to assess various AI models, data types, and their advantages. Methods A systematic literature search was conducted, encompassing studies published during 2017–2025. The review focused on research utilizing AI methodologies for predicting pancreatic cancer progression. The analysis followed a three-stage process: initial search, title and abstract screening, and full-text review. Data synthesis included the evaluation of model performance, data types, and validation strategies. Results From an initial pool of 35,577 articles, 33 met the inclusion criteria. The random forest was the most frequently applied machine learning (ML) algorithm (14/33, 42.4%). Three types of data were used: clinical data from electronic health records in 9 (27.3%) studies, radiomics in 11 (33.3%) studies, and genomics in 13 (39.4%) studies. The number of patients varied between 45 and 4846 for clinical data based models, between 70 and 1711 for genomics and between 64 and 1516 for radiomics. The resulting AUC varied between 0.933 and 0.732 for clinical data based models, between 0.671 and 0.938 for radiomics, between 0.571 and 0.92 for radiomics. All studies were heterogenous in terms of design, data feature selection and endpoints. Only 14 studies (42.4%) reported external validation of prognostic models. Ten out of thirteen genomics studies used data from open-source databases. Only 3/11 radiomics studies used unsupervised ML methods. High risk of bias was detected in 7 (21.2%) of studies. Conclusions AI demonstrates substantial potential for improving the accuracy of recurrence prediction in pancreatic cancer. However, standardization and improved accessibility are critical for facilitating clinical implementation. Further research is required to refine AI models for routine clinical use.