Artificial Intelligence in Oncology Clinical Practice: From Initial Patient Assessment to Outcome Prediction

Abstract Artificial intelligence (AI) is increasingly embedded across oncology workflows, with applications spanning initial assessment, diagnostic work-up, treatment selection, longitudinal monitoring, and survivorship care. A clinically oriented synthesis of these use cases is needed to guide real-world adoption and highlight implementation challenges. A narrative review of recent clinical, translational, and health-services literature was conducted, focusing on AI tools deployed in routine or near-term oncology practice. The manuscript organizes evidence along the cancer care continuum, from first presentation to outcome prediction, and integrates ethical, legal, and regulatory perspectives. AI systems now support ambient documentation and natural language processing (NLP) for initial consultations, advanced imaging and digital pathology for diagnosis, and multimodal decision support for systemic therapy, radiation, and surgery. Additional applications include toxicity and adverse-event prediction, real-time symptom and liquid biopsy monitoring, risk-adapted follow-up, and survivorship risk stratification, although prospective validation and interoperability remain uneven. AI has moved from proof-of-concept to a practical adjunct to oncology decision-making, with demonstrable potential to enhance precision, efficiency, and patient experience across the cancer continuum. Realizing this promise will require validated, explainable, and equitable systems, robust data governance, and deliberate design of human–AI collaboration within everyday oncology practice. AI would not be a replacement for human expertise, but a pivotal tool to aid cancer patient care.