Empowering Locally Deployable Medical Agent via State Enhanced Logical Skills for FHIR-based Clinical Tasks

• URL: http://arxiv.org/abs/2603.06902v1
• Date: Fri, 06 Mar 2026 21:42:14 GMT
• Title: Empowering Locally Deployable Medical Agent via State Enhanced Logical Skills for FHIR-based Clinical Tasks
• Abstract summary: State-Enhanced Logical-Skill Memory (SELSM) is a training-free framework that distills simulated clinical trajectories into entity-agnostic operational rules.<n> evaluated on MedAgentBench -- the only authoritative high-fidelity virtual EHR sandbox benchmarked with real clinical data.
• Score: 15.634158040789474
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While Large Language Models demonstrate immense potential as proactive Medical Agents, their real-world deployment is severely bottlenecked by data scarcity under privacy constraints. To overcome this, we propose State-Enhanced Logical-Skill Memory (SELSM), a training-free framework that distills simulated clinical trajectories into entity-agnostic operational rules within an abstract skill space. During inference, a Query-Anchored Two-Stage Retrieval mechanism dynamically fetches these entity-agnostic logical priors to guide the agent's step-by-step reasoning, effectively resolving the state polysemy problem. Evaluated on MedAgentBench -- the only authoritative high-fidelity virtual EHR sandbox benchmarked with real clinical data -- SELSM substantially elevates the zero-shot capabilities of locally deployable foundation models (30B--32B parameters). Notably, on the Qwen3-30B-A3B backbone, our framework completely eliminates task chain breakdowns to achieve a 100\% completion rate, boosting the overall success rate by an absolute 22.67\% and significantly outperforming existing memory-augmented baselines. This study demonstrates that equipping models with a dynamically updatable, state-enhanced cognitive scaffold is a privacy-preserving and computationally efficient pathway for local adaptation of AI agents to clinical information systems. While currently validated on FHIR-based EHR interactions as an initial step, the entity-agnostic design of SELSM provides a principled foundation toward broader clinical deployment.

Related papers

• FedEHR-Agents: Federated Agentic Optimization for Automated EHR Modeling [8.850916488677612]
  FedEHR-Agents is an experience-centric federated agentic optimization framework for automated EHR modeling.<n>Experiments on real-world multi-hospital EHR benchmarks demonstrate that FedEHR-Agents consistently outperforms local and federated baselines across diverse clinical prediction tasks.
  arXiv  Detail & Related papers  (2026-08-28T03:02:10Z)
• FMRP-LEAN: A HIPAA-Compliant AI-Augmented LIMS Architecture for End-to-End Clinical Assay Workflow Optimization [0.0]
  This paper presents FMRP-LEAN, a HIPAA-compliant, AI-augmented Laboratory Information Management System (LIMS) architecture that formalizes bio-specimen lifecycle management through a finite-state workflow model with explicit transition guards and dwell-time observability.
  arXiv  Detail & Related papers  (2026-07-22T17:11:52Z)
• EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs [51.129595320595094]
  Clinical decision-making (CDM) is central to real-world clinical, where clinicians infer diagnoses, select treatments, or anticipate future health outcomes under incomplete evidence.<n>LLM models are increasingly used to support these decisions due to strong language capabilities, broad biomedical knowledge, and efficiency.<n>The reliability of LLMs on real-world clinical decision tasks remains insufficiently understood.
  arXiv  Detail & Related papers  (2026-05-28T22:38:26Z)
• Agentifying Patient Dynamics within LLMs through Interacting with Clinical World Model [57.78184285979881]
  We introduce SepsisAgent, a world model-augmented LLM agent for sepsis treatment recommendation.<n>SepsisAgent uses a learned Clinical World Model to simulate patient responses under candidate fluid--vasopressor interventions.
  arXiv  Detail & Related papers  (2026-05-14T11:50:00Z)
• An Agentic LLM-Based Framework for Population-Scale Mental Health Screening [0.3568466510804538]
  This paper proposes an agentic framework for building robust LLM-based pipelines.<n>A proof-of-concept in transcript-based detection demonstrates that the framework converges to stable configurations.<n>Results highlight the potential of agentic AI to enable population-level mental health screening over large clinical datasets.
  arXiv  Detail & Related papers  (2026-05-13T06:08:43Z)
• Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration [53.772014300855375]
  We introduce NIAgent, a multi-agent system for autonomous end-to-end neuroimaging analysis.<n>Unlike conventional flat tool-calling agents, NIAgent adopts a code-centric execution paradigm.<n>We propose a hierarchical verification framework for autonomous quality control, integrating cohort-level metric screening with agentic visual inspection.
  arXiv  Detail & Related papers  (2026-05-10T06:30:19Z)
• EmboCoach-Bench: Benchmarking AI Agents on Developing Embodied Robots [68.29056647487519]
  Embodied AI is fueled by high-fidelity simulation and large-scale data collection.<n>However, this scaling capability remains bottlenecked by a reliance on labor-intensive manual oversight.<n>We introduce textscEmboCoach-Bench, a benchmark evaluating the capacity of LLM agents to autonomously engineer embodied policies.
  arXiv  Detail & Related papers  (2026-01-29T11:33:49Z)
• AgentEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarization [28.032865974227875]
  RetroSum is a framework that unifies a retrospective summarization mechanism with an evolving experience strategy.<n>It achieves performance gains of up to 29.16% over competitive baselines, while significantly decreasing total interaction errors by up to 92.3%.
  arXiv  Detail & Related papers  (2026-01-20T12:48:04Z)
• Bridging the Knowledge-Action Gap by Evaluating LLMs in Dynamic Dental Clinical Scenarios [9.865786198063644]
  The transition of Large Language Models (LLMs) from passive knowledge retrievers to autonomous clinical agents demands a shift in evaluation-from static accuracy to dynamic behavioral reliability.<n>This study empirically charts the capability boundaries of dental LLMs, providing a roadmap for bridging the gap between standardized knowledge and safe, autonomous clinical practice.
  arXiv  Detail & Related papers  (2026-01-19T11:36:39Z)
• From Performance to Practice: Knowledge-Distilled Segmentator for On-Premises Clinical Workflows [8.438143117044795]
  We present a deployment-oriented framework to translate a high-performing segmentation model into a scalable family of compact student models.<n>The proposed approach preserves architectural compatibility with existing clinical systems while enabling systematic capacity reduction.<n>Results show that knowledge distillation provides a practical and reliable pathway for converting research-grade segmentation models into maintainable, deployment-ready components.
  arXiv  Detail & Related papers  (2026-01-14T05:44:30Z)
• Bio-inspired Agentic Self-healing Framework for Resilient Distributed Computing Continuum Systems [4.003029907200818]
  ReCiSt is a bio-inspired agentic self-healing framework designed to achieve resilience in Distributed Computing Continuum Systems (DCCS)<n>ReCiSt reconstructs the biological phases of Hemostasis, Inflammation, Proliferation, and Remodeling into the computational layers Containment, Diagnosis, Meta-Cognitive, and Knowledge for DCCS.<n>These four layers perform autonomous fault isolation, causal diagnosis, adaptive recovery, and long-term knowledge consolidation through Language Model (LM)-powered agents.
  arXiv  Detail & Related papers  (2026-01-01T13:30:38Z)
• Bridging VLMs and Embodied Intelligence with Deliberate Practice Policy Optimization [72.20212909644017]
  Deliberate Practice Policy Optimization (DPPO) is a metacognitive Metaloop'' training framework.<n>DPPO alternates between supervised fine-tuning (competence expansion) and reinforcement learning (skill refinement)<n> Empirically, training a vision-language embodied model with DPPO, referred to as Pelican-VL 1.0, yields a 20.3% performance improvement over the base model.<n>We are open-sourcing both the models and code, providing the first systematic framework that alleviates the data and resource bottleneck.
  arXiv  Detail & Related papers  (2025-11-20T17:58:04Z)
• Timely Clinical Diagnosis through Active Test Selection [49.091903570068155]
  We propose ACTMED (Adaptive Clinical Test selection via Model-based Experimental Design) to better emulate real-world diagnostic reasoning.<n>LLMs act as flexible simulators, generating plausible patient state distributions and supporting belief updates without requiring structured, task-specific training data.<n>We evaluate ACTMED on real-world datasets and show it can optimize test selection to improve diagnostic accuracy, interpretability, and resource use.
  arXiv  Detail & Related papers  (2025-10-21T18:10:45Z)
• Representation Learning of Lab Values via Masked AutoEncoders [2.785172582119726]
  We propose Lab-MAE, a novel transformer-based masked autoencoder framework for imputation of sequential lab values.<n>Lab-MAE achieves equitable performance across demographic groups of patients, advancing fairness in clinical predictions.
  arXiv  Detail & Related papers  (2025-01-05T20:26:49Z)
• Medchain: Bridging the Gap Between LLM Agents and Clinical Practice with Interactive Sequence [68.05876437208505]
  We present MedChain, a dataset of 12,163 clinical cases that covers five key stages of clinical workflow.<n>We also propose MedChain-Agent, an AI system that integrates a feedback mechanism and a MCase-RAG module to learn from previous cases and adapt its responses.
  arXiv  Detail & Related papers  (2024-12-02T15:25:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.