ABSTRACT:
Clinical decision support systems, or CDSS, are essential to contemporary healthcare because they help medical practitioners make well-informed decisions in real time. In order to improve patient outcomes, this study investigates the creation of an AI-based CDSS especially intended for medication recommendations. The suggested system analyses patient data, clinical notes, and past treatment records using sophisticated machine learning algorithms and natural language processing (NLP). The system provides individualised medication recommendations by combining electronic health records (EHR) and real-time clinical data, taking into account comorbidities, patient history, and possible drug interactions. The AI-driven model improves diagnostic accuracy and reduces the likelihood of human errors by providing evidence-based recommendations. Furthermore, it enables predictive analytics to forecast possible adverse drug reactions (ADR) and optimize treatment plans accordingly. Emphasis is placed on explainability to ensure transparency and foster trust among healthcare professionals. The research also highlights the challenges involved in developing CDSS, such as data privacy concerns, interoperability issues, and bias in AI models. Preliminary experiments demonstrate the potential of the proposed system to streamline clinical workflows and enhance patient outcomes by ensuring precise, timely, and personalized drug suggestions. This study contributes to the growing body of literature on AI applications in healthcare by offering a novel framework that integrates clinical expertise with AI-driven insights. Future directions include validating the system through large-scale clinical trials and exploring the integration of federated learning models to ensure data privacy. The findings underscore the transformative potential of AI-based CDSS in advancing precision medicine and improving patient care.