Artificial Intelligence in Healthcare: A Simulation-Based Evaluation of Clinical Decision Support Systems and Future Directions

Artificial Intelligence (AI) is rapidly transforming healthcare by enhancing diagnostic accuracy, improving clinical decision-making, reducing medical errors, and enabling personalized treatment strategies. Among its applications, AI-driven Clinical Decision Support Systems (CDSS) have emerged as a critical tool for augmenting clinical practice through data-driven insights. This study adopts a simulation-based research design, supported by secondary data analysis, to evaluate the effectiveness of AI-enhanced CDSS in comparison with traditional rule-based systems. A synthetic dataset comprising 10,000 patient records was generated to simulate real-world clinical scenarios. The findings indicate that AI-based CDSS improve diagnostic accuracy by 18%, reduce decision-making time by 27%, and enhance patient outcome prediction accuracy by 22%, while significantly lowering false positive rates. Despite these advantages, challenges related to data privacy, algorithmic bias, interpretability, and regulatory uncertainty persist. The study proposes a strategic framework for sustainable AI integration in healthcare, emphasizing explainable AI, hybrid human–AI collaboration, and robust governance mechanisms. These findings contribute to the growing body of literature on AI in healthcare and provide actionable insights for policymakers, healthcare practitioners, and technology developers.

Artificial Intelligence, Clinical Decision Support Systems, Healthcare Analytics, Machine Learning, Explainable AI

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