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Agentic artificial intelligence is transitioning from experimental healthcare pilots to real-world, scalable applications. The key, experts say, lies in grounding large language models (LLMs) with factual accuracy and logical reasoning, a feat accomplished through neuro-symbolic AI. This methodology blends the intuitive strengths of LLMs with the precision of symbolic representation and logical deduction. A hybrid architecture, incorporating LLMs and reinforcement learning alongside structured knowledge bases and clinical logic, effectively mitigates the risk of inaccuracies and ensures decisions align with established medical guidelines. This is achieved by leveraging high-quality datasets, collaborative domain expertise, and the contributions of leading AI researchers. Successful deployments showcase AI’s potential: AI-driven clinical reasoning is boosting denial overturn rates, multi-agent reasoning models are expediting accurate reimbursements, and conversational AI agents are enhancing patient engagement.