Impact of Machine Learning/AI Models in Cancer Treatment

Abstract

AI/ML's foray into healthcare, especially oncology, heralds a paradigm shift in patient diagnosis, treatment, and monitoring. Historical applications have showcased AI's proficiency in interpreting complex diagnostic images, predicting treatment responses, and personalizing therapy regimens based on genetic data. However, the full integration of these technologies in cancer care remains nascent, with significant untapped potential. Through a meticulous examination of existing literature and recent case studies, this study positions itself at the forefront of efforts to harness AI/ML's full capabilities, aiming to catalyze a comprehensive adoption in cancer treatment protocols. The use of AI/ML in healthcare, particularly in oncology, has been growing, driven by advances in technology and data analytics. Studies have shown promising results in using AI/ML for diagnostic imaging, genetic profiling, and treatment efficacy prediction. Despite these advances, there remains a significant gap in the comprehensive application of these technologies across all stages of cancer care. This study situates itself within the ongoing efforts to bridge this gap, drawing on extensive literature to underscore the potential and current limitations of AI/ML in improving cancer treatment.