This study conducts a Systematic Mapping Study (SMS) to explore the application of Artificial Intelligence (AI) and Operations Research (OR) in the field of cancer prediction. By analyzing recent scientific contributions, we aim to uncover major trends, frequently adopted technologies, and the principal challenges encountered in this area. While AI performs remarkably well in prediction today, many studies rely on machine learning, deep learning, and optimization methods to enhance predictions and support clinical decision-making. OR remains confined to organizational aspects (e.g., operating room planning, allocation of clinical resources, intra-hospital transport, treatment planning such as radiotherapy, chemotherapy scheduling) 1. However, our analysis indicates that no study published in the last ten years explicitly combines AI and OR within the context of cancer prediction. Although both AI and OR have been extensively applied across various healthcare domains, their combined use specifically in cancer prediction remains largely unexplored. This gap highlights a valuable opportunity for future research to integrate both approaches, aiming to develop more robust predictive models, optimize resource utilization, and enhance the effectiveness of cancer care in real-world settings.
Oufaska et al. (Thu,) studied this question.