Clinical Decision Support Systems (CDSS) are essential tools in contemporary healthcare, enhancing
clinicians’ decisions and patient outcomes. The integration of artificial intelligence (AI) is now
revolutionizing CDSS even further. This review delves into AI technologies transforming CDSS, their
applications in healthcare decision-making, associated challenges, and the potential trajectory toward fully
realizing AI-CDSS’s potential. The review begins by laying the groundwork with a definition of CDSS and its
function within the healthcare field. It then highlights the increasingly significant role that AI is playing in
enhancing CDSS effectiveness and efficiency, underlining its evolving prominence in shaping healthcare
practices. It examines the integration of AI technologies into CDSS, including machine learning algorithms
like neural networks and decision trees, natural language processing, and deep learning. It also addresses the
challenges associated with AI integration, such as interpretability and bias. We then shift to AI applications
within CDSS, with real-life examples of AI-driven diagnostics, personalized treatment recommendations,
risk prediction, early intervention, and AI-assisted clinical documentation. The review emphasizes user-centered design in AI-CDSS integration, addressing usability, trust, workflow, and ethical and legal considerations. It acknowledges prevailing obstacles and suggests strategies for successful AI-CDSS adoption, highlighting the need for workflow alignment and interdisciplinary collaboration. The review concludes by summarizing key findings, underscoring AI’s transformative potential in CDSS, and advocating for continued research and innovation. It emphasizes the need for collaborative efforts to realize a future where AI-powered CDSS optimizes healthcare delivery and improves patient outcomes.