Transformative Role of Artificial Intelligence in Cancer Detection, Diagnosis, and Treatment Planning: A Narrative Review
DOI:
https://doi.org/10.61982/d0y7zv30Keywords:
Artificial Intelligence, Cancer Diagnosis, Deep Learning, Machine Learning, Oncology, Precision MedicineAbstract
Background: Even with the current breakthroughs in detection and treatment, cancer continues to be a leading source of disease and mortality across the world. Conventional approaches face limitations such as late detection, misdiagnosis, high treatment costs, and treatment resistance.
Objective: To examine the role of artificial intelligence in diagnosing cancer, determining its type, developing treatments, and improving the personalization of cancer treatment through advanced models and technologies.
Methods: This narrative review draws on a search conducted across major databases, including PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar, using keywords like "Artificial Intelligence," "Deep Learning," "Oncology," "Radiomics," and "Explainable AI." Reference lists of key papers were also manually checked to catch any relevant studies the database search might have missed. Only studies published between January 2015 and July 2026 were considered, covering both the foundational AI-in-oncology literature and the most recent developments in explainable AI. Studies published in peer-reviewed journals or conference proceedings, those focusing on AI, ML, or DL applications in cancer detection, diagnosis, prognosis, or treatment planning, reporting diagnostic performance, model architecture, or clinical validation outcomes, or having articles published in English were included. Studies unrelated to oncology or cancer-specific applications, conference abstracts, editorials, or opinion pieces without substantive data, duplicate publications, and studies with insufficient methodological details were excluded.
Results: This review indicates that artificial intelligence acts as a crucial aspect for the detection, classification, diagnosis, and treatment planning of cancer. Deep learning approaches, including Convolutional neural networks, and machine learning approaches such as random forests and support vector machines performed outstandingly in analyzing complex data such as imaging, pathological, clinical, and genomic data. Artificial intelligence-driven systems have the potential to improve diagnostic accuracy, enable early cancer detection, streamline radiology and pathology workflows, and support personalized medicine.
Conclusion: It can be seen that artificial intelligence has emerged as an extremely powerful tool in modern-day oncology and possesses the potential to bring about revolutionary changes in the way we identify, diagnose, and treat cancers. The use of artificial intelligence in combination with clinical expertise can help deliver much more accurate and customized treatment of cancer.
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Copyright (c) 2026 Bushra Fatima, Bushra Maqbool, Fatima Khurshid
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CMH Journal of Allied Health Sciences is published by CMH Lahore Medical College and Institute of Dentistry.
