ARTIFICIAL INTELLIGENCE IN CANCER DIAGNOSIS AND TREATMENT PLANNING: A NEW FRONTIER

Artificial intelligence (AI) has recently gained significant attention as a decision-support system used in cancer diagnosis and treatment planning. Deep learning (DL) and machine learning (ML) methods make it possible to analyze and process complex and heterogeneous medical information, such as imaging, histopathology, genomics, and electronic health records. These methods have shown promise in the area of enhancing early detection, classification of tumors, prognosis, and the choice of therapy. Nevertheless, even with the fast development, the majority of AI systems are still at the experimental or initial validation phase. Among the main issues, there is a lack of generalizability, interpretability, relying on large, high-quality data sets, and a deficiency of prospective clinical validation. Also, there are regulatory, ethical, and workflow integration obstacles which impede routine clinical adoption. This review critically assesses the existing applications of AI in oncology, distinguishing between experimental, validated, and implemented systems. Primary evidence, clinical translation, and regulatory considerations are emphasized. Further directions such as explainable AI, multimodal integration, and real-world validation are also discussed to contribute to safe, reliable, and equitable implementation in oncology practice.