Artificial Intelligence in Oncology: Applications, Challenges and Future Frontiers

This review article explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) in cancer research. It focuses on the critical role of AI in medical imaging for cancer detection, exploring deep learning algorithms for image recognition and feature extraction. The study also examines the challenges and considerations in implementing AI for image analysis in cancer diagnosis. AI is also utilized for mining and analyzing large-scale omics data, such as genomics and proteomics. It is used to decipher complex genetic mutations and signaling pathways in cancer, integrating clinical and molecular data for more accurate diagnosis and treatment planning. The review also discusses AI applications in drug discovery, target identification and drug repurposing for cancer. AI-driven algorithms are used to predict drug responses and identify novel therapeutic targets, with case studies illustrating successful applications. The review evaluates the impact of AI on clinical decision-making and patient care, highlighting the challenges and opportunities in translating AI research into clinical practice. The review concludes with future directions and innovations, exploring emerging trends and potential advancements in AI-driven cancer research. Keywords: Deep learning, Algorithm, Precision oncology, Biomarkers, Personalized cancer treatment, Predictive models