AI In Oncology , A Perspective Review from Diagnosis to Treatment
Keywords:
oncology, cancer screening, digital pathology, precision medicine, foundation models, clinical translation, health equityAbstract
Background: The application of artificial intelligence (AI) across the cancer care continuum has accelerated dramatically, driven by advances in deep learning, computational infrastructure, and the exponential growth of multimodal health data. However, the translation of algorithmic promise into meaningful clinical utility remains uneven and contested. Scope and Methods: This narrative critical review synthesises peer-reviewed literature, regulatory filings, and prospective clinical studies published between 2018 and 2026. We examined AI applications in cancer detection, diagnosis, prognostication, and treatment, with particular attention to clinical validation, implementation barriers, and equity considerations. Key Findings: While AI systems have demonstrated expert-level performance in controlled settings for mammography screening, digital pathology, and radiotherapy auto-contouring, few applications have been evaluated in prospective randomised trials. The emergence of multimodal foundation models and generative AI offers transformative potential but introduces new risks related to hallucination, liability, and workflow integration. Regulatory frameworks are evolving but remain fragmented across jurisdictions. Concerns regarding algorithmic bias, underrepresentation in training data, and cost-effectiveness gaps threaten to undermine equitable deployment. Conclusion: AI in oncology stands at an inflection point. The field must pivot from proof-of-concept studies to rigorous prospective evaluation, prioritise real-world evidence generation, and embed equity considerations at every stage of development. Without such disciplined translation, the risk of amplifying disparities while overstating clinical benefit remains substantial.
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