Abstract
Machine learning (ML) technology has rapidly advanced and transformed sectors of today's world, healthcare specifically oncology, has benefited from such technology in recent years more than any other sector. This paper reviews the implementation of ML approaches in the field of oncology, specifically for precision diagnosis and personalized treatment, both of which are vital for dealing with the high level of complexity and heterogeneity associated with cancer. ML models are improving the accuracy of cancer diagnoses and prediction of patient-specific responses to treatments and enabling novel target discovery. Yet, the adoption of ML approaches in oncology is not devoid of difficulties. Such systems come with challenges such as data privacy, ethics, algorithmic biases, and technicalities such as model explainability and deployment in existing medical infrastructure, which this paper attempts to address However, the ML tools have a huge potential to change the way we practice and help us treat patients more efficiently, effectively and in a more patient-centered way. Then, the article discusses what the future holds for ML-driven oncology, such as big data capabilities, improvements in deep learning, and to for interdisciplinary collaborations between AI developers, oncologists and researchers. As this landscape continues to evolve, such considerations and innovation will be key in addressing the relevant ethical and practical challenges of machine learning to deliver on the promise of transforming cancer diagnosis and treatment.
Published Version
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