Impact of Machine Learning on Healthcare Analytics

Nithin Narayan Koranchirath

Abstract: Machine learning is becoming more and more intertwined with the practice of medicine, encompassing a wide range of applications such as processing pre - clinical data, aiding in bedside diagnoses, stratifying patients, facilitating treatment decision - making, and providing early warning for primary and secondary prevention. However, the widespread utilization of machine learning in healthcare necessitates careful consideration of technological, medical, and ethical factors. It is crucial to thoroughly validate machine - learning - based technologies in real - life situations, impartially evaluate their benefits and risks, and avoid excessive reliance on technology that may compromise clinical, ethical, and social - related decision - making abilities. Additional challenges include the importance of meticulous benchmarking and external validations, sharing computational experts' knowledge with field users, and promoting responsible code and data sharing to enable transparent evaluation of pipelines. This review aims to emphasize the significant advancements made in integrating machine - learning platforms into clinical medicine, while also acknowledging the limitations, risks, and obstacles that must be overcome in order to further enhance the integration of learning systems in the medical field.

Keywords: Artificial intelligence, computer assisted identification and prognosis, suggestion mechanisms, customized and meticulous healthcare