Autonomous Clinical AI Platforms: Agentic Healthcare Systems, Knowledge-Augmented LLMs, and Predictive Clinical Analytics
Abstract
What happens when healthcare AI moves beyond answering questions and begins to retrieve evidence, reason through clinical information, coordinate specialised agents, predict outcomes, and support complex workflows? Autonomous Clinical AI Platforms: Agentic Healthcare Systems, Knowledge Augmented LLMs, and Predictive Clinical Analytics explores this emerging frontier from foundational technologies to next generation healthcare intelligence. The book covers agentic AI, clinical decision support, large language models, Retrieval Augmented Generation, multi agent systems, healthcare knowledge sources, and human centred AI. It examines the data foundations of intelligent healthcare, including electronic health records, clinical data architecture, data quality, governance, interoperability, FHIR, HL7, OMOP, real time processing, and AI ready platforms. Clinical knowledge intelligence receives detailed attention through medical knowledge bases, knowledge graphs, hybrid retrieval, semantic search, ClinicalRAG, hallucination mitigation, explainability, and evidence traceability. Clinical agents, autonomous reasoning, contextual intelligence, reinforcement learning, agent communication, human agent collaboration, and multi agent evaluation further illustrate how intelligent systems can operate collectively. The book also explores autonomous data pipelines, workflow orchestration, anomaly detection, self-healing systems, predictive analytics, clinical outcome prediction, population…
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