The use of artificial intelligence to target clinical problems has been applied as a revolution in clinical decision-making. Also, artificial intelligence has been made possible in all parts by using large labeled data with a significant increase in computing power and cloud storage. The success of these tools depends on understanding the intrinsic processes used along the conventional pathway by which clinicians make decisions. In the proposed work, we highlight the state-of-the-art in artificial intelligence and related approaches that can influence at four levels: For data acquisition, feature extraction, interpretation, and decision support. There are also clinical applications widely using artificial intelligence approaches that we describe them. In addition, many technical, medical, and ethical considerations are critical using artificial intelligence, among which we discuss limitations and related challenges such as explainability, regulatory, validation, etc. Ultimately, due to the increasing growth of artificial intelligence in clinical decision-making, we look into the future.