The Current State of Artificial Intelligence in Oncology

Purpose: Artificial Intelligence (AI) has evolved into a foundational component of modern oncology, reshaping prevention, diagnosis, and treatment paradigms [1-3]. This comprehensive review evaluates current validated applications, translational advances, regulatory evolution, ethical considerations, and future development of multimodal AI systems [4-7]. Methods: A systematic search of PubMed, Embase, Scopus, Web of Science, ASCO, and ESMO materials was performed (2010–2025) [8,9]. Studies with clinical validation cohorts, FDA/EMA regulatory documentation, and prospective AI-based oncology trials were prioritized [10]. Results: AI is now firmly established in early detection (liquid biopsy, multi-omics), diagnostic imaging, pathology, radiotherapy automation, immunotherapy response prediction, digital twins, and clinical decision support [1,3,6,11-15]. More than 120 FDA-cleared AI tools are applicable to oncology workflows as of 2025 [16,17]. Multimodal deep-learning systems integrating imaging, genomics, pathology, and clinical text consistently outperform unimodal architectures in external validation [7,13,18]. Despite this progress, challenges remain in algorithmic bias, generalizability, model drift, data governance, and interpretability [19-23]. Conclusion: AI will define the trajectory of personalized cancer care [2,6,7]. Harmonized regulatory frameworks, prospective trials, equity-centered deployment, and integration of large-scale multimodal datasets will be prerequisites for safe and effective implementation [16,17,21-23].