Artificial Intelligence and Medical Decision Support in Advanced Healthcare System

The improvements in deep learning (DL) and machine learning (ML) centered on enhancing the availability of medical information have stimulated renewed interests in computerized clinical decision support systems (CDSSs). These systems have indicated significant capability to enhance medical provisions, patient privacy, and service affordability. Nonetheless, the usage of the systems does not come without problems, since faulty and inadequate CDSS might deteriorate the quality of medical provisions and put the patients at potential risks. Moreover, CDSSs adoption might fail due to projected patients ignoring CDSS outputs as a result of lack of action, relevancy, and trust. The main purpose of this research is to provide the required guidance centered on the literature done for various aspects of CDSS adoption with a critical focus on DL and ML-centered systems: quality assurance, commission, acceptance, and selections.