Abstract

Machine learning (ML), a subset of artificial intelligence, is used to construct algorithms for monitoring, diagnosing, forecasting, and predicting clinical results. Health is a major concern for human beings. The current success in ML is due to deep learning (DL), using huge artificial neural networks. In the past, machine learning has demonstrated its usefulness and skills in detecting cancer. It is one of the most feasible solutions for top healthcare pioneers to detect anomalies. When healthcare companies succeed in using predictive models, they face challenges in demonstrating their value and gaining trust across the company. Recently, established standards for reporting machine learning-based clinical research will aid in connecting the clinical and computer science communities and realizing the full potential of machine learning techniques. The researchers have many objectives in the design of machine Learning Algorithms for different applications. Many papers discussed how machine learning algorithms are involved in health monitoring which will be updated so that patients, doctors, or any individuals can view the information. The main goal of this paper is to discuss basic types of Machine Learning and the challenges faced by Artificial intelligence (AI) in health care. The possible risks in clinical research give practical information on how to accurately and effectively analyze performance and avoid frequent pitfalls, particularly when dealing with applications for health and wellness contexts.