Recent Challenges in Medical Science using Machine Learning Techniques: A Review

Medical science is significantly impacted by the applications of Machine Learning (ML). ML is a subset of Artificial Intelligence (AI) technology that improves physicians' efficiency and precision. Recently, machine learning techniques have facilitated a tremendous expansion in the field of medicine. The field of medical science has been revolutionized by machine learning, which provides new methods for analyzing large-scale patient data and providing accurate predictions for diagnosis, treatment, and disease progression. The risk of developing diseases such as cancer, diabetes, and cardiovascular disease has been predicted using machine learning algorithms. These algorithms analyze large sets of patient data, including genetic information, medical history, and lifestyle factors, to recognize patterns and predict disease risk. In addition, machine learning has been used to develop individualized treatment programs for patients. By analyzing patient data, machine learning algorithms can determine the most effective treatments for each patient, thereby reducing the risk of adverse effects and enhancing patient outcomes. Moreover, machine learning techniques have been used to develop image analysis tools for medical imaging, allowing doctors to accurately diagnose diseases such as cancer and identify the location and severity of injuries. This review discusses the current challenges of medical science using machine learning techniques and how medical science problems can be solved using machine learning algorithms.