Iranian Journal of Basic Medical Sciences

Iranian Journal of Basic Medical Sciences

Artificial intelligence in cancer therapeutics: Enabling personalized and predictive treatment strategies

Articles in Press

Document Type : Review Article

Authors
Saliha Rizvi 1
Naseem Fatima 2
Nimra Haseeb 3
Tanya Bose 3
Alisha Zaidi 1
Syed Tasleem Raza 2
Farzana Mahdi 4
1 Department of Biotechnology, Era University, Lucknow, Uttar Pradesh, India
2 Department of Biochemistry, Era’s Lucknow Medical College & Hospital, Era University, Lucknow, Uttar Pradesh, India
3 MediGence, New Delhi, India
4 Department of Personalized & Molecular Medicine, Era University, Lucknow, Uttar Pradesh, India
10.22038/ijbms.2026.94878.20467
Abstract
Artificial intelligence (AI) is leading the way in revolutionizing precision oncology by allowing cancer treatments to be tailored to individual patient characteristics. The combination of AI with high-throughput sequencing, multi-omics studies, and advanced imaging technologies has paved the way for data-driven models that improve clinical decision-making in cancer care. In addition, AI-powered methods have been developed to construct patient-specific tumor growth models, utilizing vast amounts of data to forecast individual treatment responses. These models are designed to customize treatment strategies, thereby enhancing effectiveness while reducing side effects. Importantly, AI has played a crucial role in discovering biomarkers that aid in the early detection of cancer and in predicting how individual patients will respond to different treatments, including chemotherapy, radiotherapy, and immunotherapy. AI-based computational tools support the identification of actionable mutations, optimize radiotherapy planning, and enhance early cancer detection. Despite its significant potential across preclinical and clinical applications, challenges such as data heterogeneity, model interpretability, ethical concerns, and regulatory hurdles remain significant barriers to widespread clinical adoption. By addressing these limitations, AI can further refine individualized cancer treatment, minimizing adverse effects while maximizing therapeutic efficacy. This review comprehensively examines the evolving landscape of AI in precision oncology and highlights future directions to bridge the gap between innovation and clinical application.
Keywords
Artificial intelligence
Biomarker discovery
Immunotherapy response- prediction
Machine learning
Personalized cancer- treatment
Precision oncology
Subjects
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Iranian Journal of Basic Medical Sciences

Articles in Press, Accepted Manuscript
Available Online from 11 October 2026