Revolutionizing cancer care with machine learning: a comprehensive review

Abstract In healthcare, the need for machine learning (ML) analysis in tailored cancer therapy is more pronounced than ever. The escalating volume and complexity of patient data and the growing understanding of diverse cancer subtypes demand sophisticated tools for precise decision-making. ML’s ability to sift through immense datasets rapidly and identify nuanced patterns addresses this need, enabling oncologists to deliver personalized treatments considering a patient’s unique genetic makeup and evolving health status. As healthcare systems strive for efficiency and cost-effectiveness, the integration of ML improves patient outcomes. It supports resource optimization, making it an indispensable asset in the contemporary fight against cancer. This review critically examines the integration of ML in healthcare, emphasizing its significance across diverse domains. It explores personalized medicine, where ML tailor’s treatments to individual genetic profiles, and precision medicine, optimizing drug efficiency. The review focuses on the role of advanced algorithms in clinical decision support systems (CDSS) for enhancing healthcare decision-making. It addresses the various cancer-related issues in different applications, discussing ML’s role in toxicity detection, predicting treatment responses, and ultimately contributing to more effective and tailored cancer therapies. This comprehensive exploration focuses on the pivotal impact of ML in shaping modern healthcare and improving patient treatment outcomes.