This paper will present a comprehensive overview of cancer biology, including its causes, progression, and global statistical trends. It will further examine conventional diagnostic and treatment practices, highlighting their limitations in terms of accuracy, time consumption, and dependency on clinical expertise. The study will then explore how AI technologies, such as Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Computer Vision, will enhance the efficiency and reliability of cancer diagnosis and prognosis. In addition, this paper will analyze various cancer data repositories, including radiographic, genomic, pathological, and clinical datasets, which will serve as the foundation for AI-based systems. It will also discuss the emerging applications of AI in oncology, such as early cancer prediction, automated diagnosis, precision medicine, and drug discovery. Furthermore, the research will address key technical challenges, including data scarcity, model interpretability, and generalizability, along with ethical concerns such as data privacy, bias, and accountability in AI-driven decisions. Finally, the paper will emphasize the future potential of AI in transforming cancer healthcare by enabling faster, more accurate, and personalized treatment strategies, thereby improving overall patient outcomes.
Meenakshi et al. (Thu,) studied this question.