Machine learning in personalized cancer treatment: Implications for global public health

Cancer remains a leading cause of mortality worldwide, posing a significant challenge to global public health. Traditional approaches to cancer treatment often adopt a "one-size-fits-all" strategy, which may not be effective for every patient due to the complex and heterogeneous nature of the disease. Personalized cancer treatment aims to tailor therapeutic interventions to the individual characteristics of each patient, enhancing the efficacy and reducing adverse effects. In recent years, machine learning (ML) has emerged as a powerful tool in personalized cancer care, offering the potential to revolutionize diagnosis, prognosis, and treatment by leveraging vast amounts of biomedical data. This review explores the current landscape of machine learning applications in personalized cancer treatment, including its role in imaging, genomics, and drug discovery. We provide an overview of key machine learning techniques, highlight successful case studies from both developed and developing nations, and examine the challenges and limitations associated with the integration of these technologies into clinical practice. Furthermore, we discuss the implications of machine learning-driven personalized cancer care for global public health, emphasizing its potential to address disparities in access to healthcare and improve outcomes in resource-limited settings. Finally, we offer insights into future research directions and policy considerations that could accelerate the adoption of machine learning in global cancer treatment, fostering a more equitable and effective healthcare ecosystem.'