Introduction. Pancreatic cancer is the 7th leading cause of cancer mortality and the 14th leading cause of incidence, due to late diagnosis and an aggressive clinical course. Prognostic models predict that pancreatic cancer will become the second leading cause of cancer mortality by 2030. Traditional recurrence prediction nomograms demonstrate limited effectiveness (C-index 0.656–0.734). Artificial intelligence technologies offer fundamentally new opportunities for early diagnosis, recurrence prediction, and therapy personalization. Their clinical implementation is associated with medical, social, ethical, and legal challenges that require a systematic analysis. Purpose. To systematize the results of studies on the use of artificial intelligence to predict pancreatic cancer recurrence and progression based on clinical parameters, assess the potential impact of these approaches on clinical practice, and consider the medical, social, and ethical aspects of implementing AI technologies in oncology. Materials and Methods. A systematic analysis of 41 scientific publications (38 English-language, 3 Russian-language) from PubMed, Web of Science, Scopus, eLIBRARY.RU, and CyberLeninka databases for 2018–2025 was conducted. Results. The obtained data demonstrate the high diagnostic accuracy of deep learning algorithms. Their effectiveness in detecting pancreatic cancer reaches 95.47%. Urinary biomarker analysis models show an area under the curve (AUC) of 0.94. Systems for predicting the malignancy of intraductal papillary mucinous tumors demonstrate an AUC of 0.98, with a sensitivity of 95.7% and a specificity of 92.6%. These figures significantly exceed the results of expert diagnosis by physicians (56.0%). Random forest and Cox proportional hazards models for predicting postoperative recurrence achieve a concordance index of 0.7738, outperforming traditional nomograms in prediction accuracy. Artificial neural networks estimate eight-month survival for unresectable cancer with an AUC of 0.921. Logistic regression shows only 0.849, with statistically significant differences. Discussion. The analysis revealed uneven geographic and socioeconomic access to artificial intelligence technologies. Differences in computing infrastructure and specialist training create barriers to implementation. Interpretability of deep learning algorithms remains a significant challenge. The black-box effect hinders the development of clinical trust in systems. Explainable artificial intelligence methods include SHAP, LIME, and gradient activation maps. They partially address this issue by visualizing the contribution of individual features to the final prognosis. Ethical issues cover a wide range of risks. Systems can reproduce systematic errors from training data. High risk assessments cause psychosocial consequences for patients. Information privacy concerns pose a threat of potential stigmatization for patients. Conclusions. Artificial intelligence outperforms traditional methods for diagnosing pancreatic cancer. Its accuracy is significantly higher compared to nomograms and expert assessments. Successful implementation requires ensuring equal access through the development of infrastructure and educational programs. Improved interpretability of algorithms for clinical use is necessary. The regulatory framework should be adapted to include mechanisms for validation and accountability. Systems require validation in various population groups to confirm their universality. Developing protocols for communicating risks to patients is a priority. Until specific legislation is adopted, artificial intelligence systems function as decision support tools for physicians. Responsibility for the final diagnosis and treatment remains with the medical professional.