Identification of prognosis changing events is essential during cancer surveillance. Current clinical practice is limited by “one-size-fits-all” time management or subjective assessment that is not necessarily appropriate for individual patients’ risk profiles. A risk-adapted follow-up is more efficient and optimal from the clinical perspective. Herein we introduce an artificial intelligence-based data-driven solution for risk-adapted follow-up schedule that devises the number of follow-up visits per year and assesses the cancer-specific risk profile over a long interval depending on the age at diagnosis. We utilized Surveillance, Epidemiology, and End Results (SEER), a cancer registry database and a resource for cancer-specific survival estimation in the United States that includes more than 2 million patients diagnosed with urologic cancers. We tested different machine learning algorithms on their definition for the feature importance and selected clinically relevant parameters regularly used in clinical routine to develop a survival modeling. As a result, the underlying recurrent neural network algorithm for follow-up modeling was fitted on the unseen test set with an overall concordance index score of 0.80. A controlled access to the online tool is available for physicians.