Artificial Intelligence (AI) and Machine Learning (ML) have significantly transformed the field of healthcare, particularly in cancer detection, diagnosis, and treatment. With the advancement of digital pathology, large clinical datasets, and powerful computational techniques, AI has become a crucial tool in oncology research and clinical practice. Deep learning algorithms can analyse high-resolution histopathology images, genomic data, and electronic health records to detect patterns that may not be visible to human experts. These technologies enable early cancer detection, risk prediction, accurate diagnosis, and personalized treatment planning. Additionally, AI-based approaches such as Natural Language Processing (NLP), radiomics, and biomarker discovery have enhanced the analysis of complex medical data. Cloud-based AI platforms further facilitate large-scale data processing and collaborative research. Despite these benefits, the integration of AI into cancer care also faces technical and ethical challenges, including data privacy concerns, lack of standardized datasets, algorithm bias, and interpretability issues. This paper explores the applications of AI in cancer prediction, diagnosis, and treatment while discussing the technical and ethical challenges associated with its implementation. The study highlights the future potential of AI-driven precision medicine and its role in improving cancer care outcomes.
Meenakshi et al. (2026) studied this question.