Machine Learning (ML) Techniques in Healthcare Systems: A Mini Review

Abstract

Artificial intelligence (AI) has made its own place in the present world. Almost in every field, AI is being utilized for betterment and advancement. Machine learning (ML) is a part of AI and has been applied extensively currently in various fields of science and technology including healthcare system. ML is the technique that uses AI to analyze, interpret and make decisions. To summarize the applications of ML in various healthcare systems in order to understand the strength and loopholes of the use of ML in medical science. The mechanisms and methods of ML approach in various medical issues have been analyzed and discussed. ML technique is being used to make decisions in medical cases, for determining the treatment regime of a particular patient, for designing and developing drugs, in personalized medicine, in designing and selecting diagnoses for any particular disease, for automated tracking of patient's recovery. Available clinical data and history are being used by ML techniques to compare, classify, select and execute results for any task being assigned. In a nutshell, ML uses earlier available information and data about the disease, the treatment protocols followed, and the results in correspondence with the clinical symptoms and pathological findings. Several achievements using ML in the healthcare system, yielded significant novel results that have been patented. There have been several thousand patents in the field of application of ML in healthcare systems from the years 2012 to 2023. Though, ML in healthcare comes with some risks and unknown possibilities yet, restricted and monitored application of ML in healthcare may hasten the healthcare system, save time, help to make efficient decisions in non-invasive ways, and may open up new possibilities in the healthcare system.