A Systematic Review on Artificial Intelligence in Cancer Prediction

Author(s)	Dr. Nidhi Gautam, Dr. Megha Sharma, Dr. Naina Aggarwal, Dr. Shivam Grover
Country	India
Abstract	Artificial Intelligence (AI) is rapidly transforming cancer care by enabling accurate, timely, and personalized predictions. This systematic review examines the role of AI in cancer prediction across imaging, genomics, histopathology, and prognosis. A structured literature search of PubMed, Scopus, Web of Science, and IEEE Xplore from 2015–2025 identified 110 relevant studies. Evidence suggests that AI models, including deep learning (DL) and machine learning (ML), achieve higher predictive accuracy than conventional diagnostic approaches, with reported sensitivities ranging from 85% to 98% in breast, lung, and colorectal cancers. AI is especially effective in early detection, tumor Classification, biomarker identification, and survival estimation. Despite advancements, challenges include limited external validation, data bias, interpretability issues, and regulatory barriers. Future research must prioritize explainable AI, multi-modal data integration, and large-scale clinical trials.
Keywords	Artificial Intelligence, Cancer Prediction, Machine Learning, Deep Learning, Oncology, Systematic Review
Field	Medical / Pharmacy
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-09-07
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.55405

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