APPLICATION OF AGENTIC AI AND CLINICAL REASONING MODELS IN CLINICAL WORKFLOW: A SYSTEMATIC IMPLEMENTATION STUDY OF DOCTORASSIST.AI

Abstract

The integration of artificial intelligence (AI) in healthcare has evolved from rule-based decision support to sophisticated agentic AI systems that reason, learn, and adapt dynamically to clinical environments. This study evaluates the implementation of clinical reasoning models and agentic AI within DoctorAssist.AI, assessing their impact on diagnostic accuracy, workflow efficiency, and clinical decision-making across 15 tertiary care centers. The agentic AI system employs Bayesian networks, causal inference models, and pattern recognition algorithms to facilitate complex case analysis, contextual adaptation, and interactive learning. The results demonstrate a 94.8% diagnostic accuracy rate, 35% reduction in diagnostic decision time, and 28% improvement in resource utilization, highlighting the transformative potential of agentic AI in modern clinical practice.