Integration of Machine Learning in Clinical Decision Support Systems

Abstract

Clinical decision support (CDS) systems offer healthcare professionals real-time, evidence-based assistance for the diagnosis, treatment, and monitoring of medical disorders, which has the potential to enhance patient outcomes. The application of machine learning algorithms in CDS systems has increased the reach and precision of these systems, enabling the examination of intricate patient data and the discovery of as-yet-unrecognized connections and patterns. This review article focuses on machine learning algorithms' uses in diagnosis and disease categorization, treatment selection and optimization, and patient monitoring and prognosis in order to examine the advantages and drawbacks of utilizing them in CDS systems. It also looks at the moral and legal issues surrounding the use of these systems, such as privacy issues and responsibility for choices made utilizing CDS technologies. A discussion of potential paths and difficulties for applying machine learning algorithms in CDS systems finishes the review. The incorporation of real-time data streams, the creation of more understandable algorithms, and the inclusion of patient preferences and values in decision-making are all possible ways to enhance CDS systems using machine learning. The necessity for thorough validation and regulatory control, as well as worries about the possible impact on clinician-patient relationships, are obstacles and difficulties to widespread implementation in clinical practice. The potential advantages of applying machine learning algorithms to CDS systems are highlighted in this paper, but it also stresses the need to address moral and legal issues and make sure that these systems are used in a responsible and open manner.