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.
Keywords: Machine Learning , Health care , Applications , Challenges in AI .
