The role of artificial intelligence (AI) in cancer care has evolved in the face of ageing population, workforce shortages and technological advancement. Despite recent uptake in AI research and adoption, the extent to which it improves quality, efficiency and equity of care beyond cancer diagnostics is uncertain to date. Henceforth, the objective of our systematic review is to assess the clinical readiness and deployability of AI through evaluation of prospective studies of AI in cancer care following diagnosis. We undertook a systematic review to determine the types of AI involved and their respective outcomes. A PubMed and Web of Science search between 1 January 2013 and 1 May 2023 identified 15 articles detailing prospective evaluation of AI in postdiagnostic cancer pathway. We appraised all studies using Risk of Bias Assessment of Randomised Controlled Trials and Risk of Bias In Non-randomised Studies-of Interventions quality assessment tools, as well as implementational analysis concerning time, cost and resource, to ascertain the quality of clinical evidence and real-world feasibility of AI. The results revealed that the majority of AI oncological research remained experimental without prospective clinical validation or deployment. Most studies failed to establish clinical validity and to translate measured AI efficacy into beneficial clinical outcomes. AI research are limited by lack of research standardisation and health system interoperability. Furthermore, implementational analysis and equity considerations of AI were largely missing. To overcome the triad of low-level clinical evidence, efficacy-outcome gap and incompatible research ecosystem for AI, future work should focus on multicollaborative AI implementation research designed and conducted in accordance with up-to-date research standards and local health systems.