Agentic AI for Clinical Decision Support: Real-Time Diagnosis, Triage, and Treatment Planning

Abstract

Clinical decision-making often involves navigating complex data, rapidly changing patient conditions, and the need for precise, context-aware recommendations. Traditional clinical decision support systems (CDSS) provide rule-based assistance but frequently fall short in dynamic environments due to their lack of contextual understanding, adaptability, and real-time responsiveness. In this paper, we introduce an agentic AI framework designed specifically for clinical environments. By integrating autonomous AI agents with medical reasoning capabilities, seamless access to electronic health records (EHRs), and structured inter-agent communication, our system enables real-time diagnosis, triage prioritization, and personalized treatment planning. Using synthetic yet medically realistic patient datasets, we evaluate system performance in terms of diagnostic accuracy, triage precision, interpretability, system latency, and physician satisfaction. The results demonstrate substantial performance gains over traditional systems and lay the groundwork for a new era in clinical AI. The complexity of clinical decision-making continues to grow due to rapidly changing patient data and the demand for timely, context-aware responses. Traditional clinical decision support systems (CDSS) often fall short in dynamic environments. In this paper, we introduce an agentic AI framework that integrates autonomous agents with structured memory and EHR interoperability. The system supports real-time diagnosis, triage, and treatment planning. In comparative evaluations using synthetic patient datasets, our framework achieved 92.4% diagnostic accuracy, 95.2% triage precision, and reduced average response latency to 3.7 seconds—a 40% improvement over rule-based CDSS. The results highlight the transformative potential of agentic AI in augmenting clinical workflows.