Reinventing Clinical Dialogue: Agentic Paradigms for LLM Enabled Healthcare Communication
Clinical dialogue represents a complex duality requiring both the empathetic fluency of natural conversation and the rigorous precision of evidence-based medicine. While Large Language Models possess unprecedented linguistic capabilities, their architectural reliance on reactive and stateless processing often favors probabilistic plausibility over factual veracity. This structural limitation has catalyzed a paradigm shift in medical AI from generative text prediction to agentic autonomy, where the model functions as a central reasoning engine capable of deliberate planning and persistent memory. Moving beyond existing reviews that primarily catalog downstream applications, this survey provides a first-principles analysis of the cognitive architecture underpinning this shift. We introduce a novel taxonomy structured along the orthogonal axes of knowledge source and agency objective to delineate the provenance of clinical knowledge against the system's operational scope. This framework facilitates a systematic analysis of the intrinsic trade-offs between creativity and reliability by categorizing methods into four archetypes: Latent Space Clinicians, Emergent Planners, Grounded Synthesizers, and Verifiable Workflow Automators. For each paradigm, we deconstruct the technical realization across the entire cognitive pipeline, encompassing strategic planning, memory management, action execution, collaboration, and evolution to reveal how distinct architectural choices balance the tension between autonomy and safety.
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Xiaoquan Zhi
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2025-12-11T10:53:11.108Z
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Xiaoquan Zhi
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693a377e74fced5bf9c3240c
Hongke Zhao
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Likang Wu
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2025-12-11T10:53:20.434Z
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likang wu
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likang03
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Chuang Zhao
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2025-12-11T10:13:28.222Z
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ChuangZhao
ChuangZhao
user
ChuangZhao
693a377e74fced5bf9c3240f
Hengshu Zhu
Reinventing Clinical Dialogue: Agentic Paradigms for LLM Enabled Healthcare Communication
Clinical dialogue represents a complex duality requiring both the empathetic fluency of natural conversation and the rigorous precision of evidence-based medicine. While Large Language Models possess unprecedented linguistic capabilities, their architectural reliance on reactive and stateless processing often favors probabilistic plausibility over factual veracity. This structural limitation has catalyzed a paradigm shift in medical AI from generative text prediction to agentic autonomy, where the model functions as a central reasoning engine capable of deliberate planning and persistent memory. Moving beyond existing reviews that primarily catalog downstream applications, this survey provides a first-principles analysis of the cognitive architecture underpinning this shift. We introduce a novel taxonomy structured along the orthogonal axes of knowledge source and agency objective to delineate the provenance of clinical knowledge against the system's operational scope. This framework facilitates a systematic analysis of the intrinsic trade-offs between creativity and reliability by categorizing methods into four archetypes: Latent Space Clinicians, Emergent Planners, Grounded Synthesizers, and Verifiable Workflow Automators. For each paradigm, we deconstruct the technical realization across the entire cognitive pipeline, encompassing strategic planning, memory management, action execution, collaboration, and evolution to reveal how distinct architectural choices balance the tension between autonomy and safety.
The survey analyzes the cognitive architecture of medical AI systems, focusing on the shift from generative text prediction to agentic autonomy, and categorizes methods into four archetypes based on knowledge source and agency objective.
Large Language Models reactive processing stateless processing agentic autonomy cognitive architecture Latent Space Clinicians Emergent Planners Grounded Synthesizers Verifiable Workflow Automators strategic planning memory management action execution collaboration evolution