In oncology, recurrence after treatment is one of the major challenges, related to patients' survival and quality of life. Conventionally, prediction of cancer relapse has always relied on clinical observation with statistical model support, which almost f ails to explain the complex, multifactorial nature of tumor recurrence. This research explores how AI and ML models may incre ase the accuracy and reliability of recurrence prediction in cancer. Therefore, AI and ML create new opportunities not only for pe rsonalized medicine but also for proactive management of patients through analyzing large volumes of data on genetics, clinic al manifestations, and treatment. The paper describes the various AI and ML techniques for pattern identification and outcome predi ction in cancer patients using supervised and unsupervised learning. Clinical implications provide an opportunity to review how early interventions could happen and the design of treatment planning.

A team of researchers from the RIKEN Center for Advanced Intelligence Project (Saitama, Japan) has developed a machine-learning platform capable of identifying features associated with prostate cancer recurrence in pathology images that were previously unknown to clinicians. In combination with pathologist-developed criteria, the technology may allow for more accurate cancer recurrence predictions. Conventionally, when clinicians and/or researchers train artificial intelligence (AI) systems, the technology is only able to learn and make predictions based on the information that has been inputted – there is no scope for the system to learn outside of what is currently known. In this study, no medical knowledge was inputted into the platform, rather, investigators employed'unsupervised' deep neural networks, called autoencoders, and utilized a subset of 13,188 non-annotated, whole-mount, diagnostic prostate pathology slide images from the Nippon Medical School Hospital (Tokyo, Japan), allowing the AI system to learn and make predictions independently. The team developed a method for translating the features identified by the machine-learning platform into high-resolution images that could be understood by clinicians.