Artificial Intelligence in Clinical Oncology: Current Innovations, Translational Challenges, and Future Directions—A Narrative Review

Background: Artificial intelligence (AI) is increasingly applied across the oncology pathway, from cancer detection and diagnosis to prognostic prediction, treatment optimization, monitoring, and clinical decision support. However, the growing and heterogeneous body of evidence makes it difficult to distinguish technical advances from demonstrated clinical benefit. Objective: To provide a narrative synthesis of AI in oncology structured around three dimensions: innovations, challenges, and future directions, with particular attention to clinical applicability, methodological limitations, and the conditions required for successful clinical translation. Methods: A narrative review was conducted using PubMed and Scopus, focusing primarily on secondary literature, including systematic reviews, meta-analyses, and other relevant evidence syntheses. A total of 122 secondary studies were analyzed. Selected primary studies were additionally considered in the Discussion to illustrate clinical evaluation, implementation, and real-world applications. The review was reported in accordance with the ANDJ Narrative Review Checklist. No systematic-review protocol, formal risk-of-bias assessment, or quantitative meta-analysis was undertaken. Results: The reviewed literature describes advances in AI-supported cancer detection and characterization, imaging and digital pathology, risk stratification, prognostic prediction, precision oncology, and treatment-related decision support. Multimodal data integration, generative AI, and federated learning represent additional areas of development. At the same time, data quality and representativeness, external validation, generalizability, reproducibility, clinical utility, workflow integration, privacy, and governance remain major challenges. Future progress will require standardized clinical evaluation, reliable and interpretable models, stronger validation across populations and settings, and integration into clinical workflows. The selected primary studies provide complementary examples of clinical evaluation and implementation but do not constitute a comprehensive assessment of clinical effectiveness. Conclusions: AI offers substantial opportunities to support oncology research and care, but its clinical value cannot be inferred from technical performance alone. Progress towards reliable and patient-centred applications will depend on robust validation, demonstrated clinical utility, safe implementation, and evidence of meaningful benefits for patients and healthcare professionals. Further focused systematic reviews may help clarify specific clinical applications and methodological questions as the evidence base evolves.