Unlocking the Potential of AI in Cancer Therapeutics: Advancements in Treatment Selection, Swift Diagnosis, Risk Assessment, and Prognosis

Cancer poses a significant challenge in terms of treatment due to its aggressive nature and low median survival rates, making accurate early diagnosis and prognosis prediction crucial for improving patient outcomes. Advances in statistics and computer engineering have led to the application of computational methods, including multivariate statistical analysis, to analyze cancer prognosis. Artificial intelligence (AI) has emerged as a transformative force in the healthcare industry, leveraging intricate pattern recognition in medical data to enhance the precision, efficacy, quality, and accuracy of radiation treatment for cancer patients. AI finds application across various critical areas in healthcare, including neurology, cardiology, and oncology, utilizing both structured and unstructured healthcare data. Its roles extend to early detection, diagnosis, treatment, outcome prediction, and prognosis evaluation, particularly in the context of cancer. Despite the potential benefits, integrating AI into clinical practice in radiation oncology faces obstacles that must be overcome. The incorporation of AI, particularly machine learning and deep learning, into clinical cancer research has significantly improved predictive performance. This review explores the literature on the application of AI in cancer diagnosis and prognosis, emphasizing the inherent advantages it offers. While recognizing the importance of rigorous validation, the studies highlight ongoing efforts to integrate AI technology into clinical settings, shaping the future of cancer care. Moreover, the review delves into future directions for AI in cancer therapy, providing insights into upcoming trends, potential developments, and emerging technologies within the AI landscape. By acknowledging the necessity for continued research and validation, the article underscores the momentum toward leveraging AI in clinical oncology and its potential to redefine the landscape of cancer diagnosis and treatment.