Paper List
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Nyxus: A Next Generation Image Feature Extraction Library for the Big Data and AI Era
This paper addresses the core pain point of efficiently extracting standardized, comparable features from massive (terabyte to petabyte-scale) biomedi...
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Topological Enhancement of Protein Kinetic Stability
This work addresses the long-standing puzzle of why knotted proteins exist by demonstrating that deep knots provide a functional advantage through enh...
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A Multi-Label Temporal Convolutional Framework for Transcription Factor Binding Characterization
This paper addresses the critical limitation of existing TF binding prediction methods that treat transcription factors as independent entities, faili...
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Social Distancing Equilibria in Games under Conventional SI Dynamics
This paper solves the core problem of proving the existence and uniqueness of Nash equilibria in finite-duration SI epidemic games, showing they are a...
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Binding Free Energies without Alchemy
This paper addresses the core bottleneck of computational expense in Absolute Binding Free Energy calculations by eliminating the need for numerous al...
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SHREC: A Spectral Embedding-Based Approach for Ab-Initio Reconstruction of Helical Molecules
This paper addresses the core bottleneck in cryo-EM helical reconstruction: eliminating the dependency on accurate initial symmetry parameter estimati...
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Budget-Sensitive Discovery Scoring: A Formally Verified Framework for Evaluating AI-Guided Scientific Selection
This paper addresses the critical gap in evaluating AI-guided scientific selection strategies under realistic budget constraints, where existing metri...
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Probabilistic Joint and Individual Variation Explained (ProJIVE) for Data Integration
This paper addresses the core challenge of accurately decomposing shared (joint) and dataset-specific (individual) sources of variation in multi-modal...
Reinventing Clinical Dialogue: Agentic Paradigms for LLM‑Enabled Healthcare Communication
College of Management and Economics, Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University | Computer Network Information Center, Chinese Academy of Sciences
30秒速读
IN SHORT: This paper addresses the core challenge of transforming reactive, stateless LLMs into autonomous, reliable clinical dialogue agents capable of longitudinal patient management and verifiable decision-making within the stringent safety constraints of healthcare.
核心创新
- Methodology Introduces a novel taxonomy for clinical dialogue agents based on two orthogonal axes: Knowledge Source and Agency Objective, categorizing systems into four distinct paradigms (Latent Space Clinicians, Emergent Planners, Grounded Synthesizers, Verifiable Workflow Automators).
- Methodology Provides a first-principles, in-depth analysis of the core cognitive components (planning, memory, action, collaboration, evolution) across the proposed paradigms, moving beyond surface-level application cataloging.
- Theory Formalizes clinical dialogue as a Partially Observable Markov Decision Process (POMDP), mathematically framing the agent's task as state estimation and long-term utility optimization, bridging abstract AI theory with clinical rigor.
主要结论
- The agentic paradigm, operationalized through five core components (planning, memory, action, collaboration, evolution), is essential to overcome the reactive and stateless limitations of foundational LLMs for complex clinical tasks.
- A taxonomy based on Knowledge Source and Agency Objective reveals fundamental trade-offs between creativity/reliability and autonomy/safety, providing a systematic framework for designing clinical agents (analysis based on a review of over 300 papers).
- Effective clinical dialogue requires modeling the interaction as a POMDP to handle information asymmetry, where the agent must maintain a belief state (b_t) and optimize a policy (π*) for long-term clinical utility, as formalized in Eq. 1 and Eq. 2.
摘要: 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. Furthermore, we bridge abstract design philosophies with the pragmatic implementation ecosystem. By mapping real-world applications to our taxonomy and systematically reviewing benchmarks and evaluation metrics specific to clinical agents, we provide a comprehensive reference for future development. Finally, we identify critical frontiers regarding trustworthiness, outlining a roadmap for future research to foster reliable and ethically aligned healthcare AI. The latest papers and related resources are maintained on our website.