AI-Driven Clinical Decision Support Systems Enhancing Decision-Making in Modern Healthcare

Abstract

The incorporation of AI into clinical CDSS enables real-time insights which can be acted upon, thus improving the quality of care provided to the patients. The architecture as well as the features and development methods of CDSS focusing on machine learning and natural language processing as well as other predictive analytics capabilities are analysed. It discusses relevant cases that allow performing medical diagnosis and prescription of medicines so as to mitigate automation-enabled medical negligence and malpractice. Also, it addresses concerns with integration of AI into clinical workflows such as data silos, clinician gatekeeping, social contexts, and legal contexts. With the use of case studies, this chapter illustrates the effect of AI tools on CDSS by showing the improvement in the heightened healthcare opportunities that are readily available after the use of AI, as well as the provision of individualized treatment meant for specific unique patients due to the flexible nature offered by AI.