The article "AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential" by Malek Elhaddad and Sara Hamam explores the integration of artificial intelligence (AI) into Clinical Decision Support Systems (CDSS) and its impact on healthcare. CDSS are essential tools that enhance clinicians' decisions and patient outcomes, and the integration of AI is revolutionizing these systems. The review covers the definition and function of CDSS, the role of AI in enhancing their effectiveness and efficiency, and the challenges associated with AI integration, such as interpretability and bias. The article highlights the integration of AI technologies, including machine learning algorithms, natural language processing (NLP), and deep learning, into CDSS. These technologies enable CDSS to process and interpret vast amounts of healthcare data, providing personalized recommendations and improving diagnostic accuracy. Real-life examples of AI-driven diagnostics, personalized treatment recommendations, risk prediction, early intervention, and AI-assisted clinical documentation are discussed. The review also emphasizes the importance of user-centered design in AI-CDSS integration, addressing usability, trust, workflow, and ethical and legal considerations. It acknowledges the challenges of integrating AI into clinical practice, such as technical limitations, regulatory constraints, and ethical considerations, and suggests strategies for successful adoption, including workflow alignment and interdisciplinary collaboration. The article concludes by summarizing key findings, underscoring the transformative potential of AI in CDSS and advocating for continued research and innovation. It highlights the need for collaborative efforts to realize a future where AI-powered CDSS optimize healthcare delivery and improve patient outcomes.The article "AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential" by Malek Elhaddad and Sara Hamam explores the integration of artificial intelligence (AI) into Clinical Decision Support Systems (CDSS) and its impact on healthcare. CDSS are essential tools that enhance clinicians' decisions and patient outcomes, and the integration of AI is revolutionizing these systems. The review covers the definition and function of CDSS, the role of AI in enhancing their effectiveness and efficiency, and the challenges associated with AI integration, such as interpretability and bias. The article highlights the integration of AI technologies, including machine learning algorithms, natural language processing (NLP), and deep learning, into CDSS. These technologies enable CDSS to process and interpret vast amounts of healthcare data, providing personalized recommendations and improving diagnostic accuracy. Real-life examples of AI-driven diagnostics, personalized treatment recommendations, risk prediction, early intervention, and AI-assisted clinical documentation are discussed. The review also emphasizes the importance of user-centered design in AI-CDSS integration, addressing usability, trust, workflow, and ethical and legal considerations. It acknowledges the challenges of integrating AI into clinical practice, such as technical limitations, regulatory constraints, and ethical considerations, and suggests strategies for successful adoption, including workflow alignment and interdisciplinary collaboration. The article concludes by summarizing key findings, underscoring the transformative potential of AI in CDSS and advocating for continued research and innovation. It highlights the need for collaborative efforts to realize a future where AI-powered CDSS optimize healthcare delivery and improve patient outcomes.