Standardization of Neuromuscular Reflex Analysis -- Role of Fine-Tuned Vision-Language Model Consortium and OpenAI gpt-oss Reasoning LLM Enabled Decision Support System

• URL: http://arxiv.org/abs/2508.12473v1
• Date: Sun, 17 Aug 2025 19:13:27 GMT
• Title: Standardization of Neuromuscular Reflex Analysis -- Role of Fine-Tuned Vision-Language Model Consortium and OpenAI gpt-oss Reasoning LLM Enabled Decision Support System
• Abstract summary: We propose a Fine-Tuned Vision-Language Model (VLM) Consortium and a reasoning Large-Language Model (LLM)-enabled Decision Support System for automated H-reflex waveform interpretation and diagnosis.
• Score: 1.5217170888985943
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate assessment of neuromuscular reflexes, such as the H-reflex, plays a critical role in sports science, rehabilitation, and clinical neurology. Traditional analysis of H-reflex EMG waveforms is subject to variability and interpretation bias among clinicians and researchers, limiting reliability and standardization. To address these challenges, we propose a Fine-Tuned Vision-Language Model (VLM) Consortium and a reasoning Large-Language Model (LLM)-enabled Decision Support System for automated H-reflex waveform interpretation and diagnosis. Our approach leverages multiple VLMs, each fine-tuned on curated datasets of H-reflex EMG waveform images annotated with clinical observations, recovery timelines, and athlete metadata. These models are capable of extracting key electrophysiological features and predicting neuromuscular states, including fatigue, injury, and recovery, directly from EMG images and contextual metadata. Diagnostic outputs from the VLM consortium are aggregated using a consensus-based method and refined by a specialized reasoning LLM, which ensures robust, transparent, and explainable decision support for clinicians and sports scientists. The end-to-end platform orchestrates seamless communication between the VLM ensemble and the reasoning LLM, integrating prompt engineering strategies and automated reasoning workflows using LLM Agents. Experimental results demonstrate that this hybrid system delivers highly accurate, consistent, and interpretable H-reflex assessments, significantly advancing the automation and standardization of neuromuscular diagnostics. To our knowledge, this work represents the first integration of a fine-tuned VLM consortium with a reasoning LLM for image-based H-reflex analysis, laying the foundation for next-generation AI-assisted neuromuscular assessment and athlete monitoring platforms.

Related papers

• Concept-Grounded Reasoning with Prompt-Driven Localization for Interpretable Structured Report Generation [66.62662330558906]
  CORAL is a multimodal framework that integrates spatial grounding and concept-level supervision into a unified reasoning process.<n>CORAL employs a prompt-driven medical segmentation model to localize lesions and predicts multi-class clinical attributes through a Concept Bottleneck module.<n>Experiments on BUS-CoT and IU X-ray datasets demonstrate consistent improvements in diagnostic accuracy, concept consistency, and report quality over strong general-purpose and medical MLLMs.
  arXiv  Detail & Related papers  (2026-09-14T10:21:23Z)
• WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning [30.134429515719905]
  We propose WEQA, a query-adaptive agent framework that unifies LLM reasoning with specialized wearable analytical and modeling tools.<n>We also curate a benchmark spanning four open wearable datasets comprising analytic and predictive tasks in three different health domains.<n>Experiments show that our framework is 24% more accurate than LLM and agentic baselines, and a blinded study with 12 medical experts and 8 users shows substantial gains in usefulness and clinical soundness.
  arXiv  Detail & Related papers  (2026-06-16T16:45:56Z)
• 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)
• Synergizing Discriminative Exemplars and Self-Refined Experience for MLLM-based In-Context Learning in Medical Diagnosis [4.619078510367921]
  Clinician Mimetic is a novel In-Context Learning (ICL) framework designed to synergize Discriminative Exemplar Coreset Selection (DECS) and Self-Refined Experience Summarization (SRES)<n> DECS simulates a clinician's ability to reference "anchor cases" by selecting discriminative visual coresets from noisy data at the computational level.<n>SRES mimics the cognition and reflection in clinical diagnosis by distilling diverse rollouts into a dynamic textual Experience Bank.
  arXiv  Detail & Related papers  (2026-03-29T15:29:59Z)
• Clinical Cognition Alignment for Gastrointestinal Diagnosis with Multimodal LLMs [63.535652574541764]
  Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in medical image analysis.<n>Their application in gastrointestinal endoscopy is currently hindered by two critical limitations.<n>We propose a novel Clinical-Cognitive-Aligned (CogAlign) framework to address these challenges.
  arXiv  Detail & Related papers  (2026-03-21T07:47:37Z)
• A Federated and Parameter-Efficient Framework for Large Language Model Training in Medicine [59.78991974851707]
  Large language models (LLMs) have demonstrated strong performance on medical benchmarks, including question answering and diagnosis.<n>Most medical LLMs are trained on data from a single institution, which faces limitations in generalizability and safety in heterogeneous systems.<n>We introduce the model-agnostic and parameter-efficient federated learning framework for adapting LLMs to medical applications.
  arXiv  Detail & Related papers  (2026-01-29T18:48:21Z)
• A Unified XAI-LLM Approach for EndotrachealSuctioning Activity Recognition [0.1794226570005898]
  This study proposes a unified framework for video-based activity recognition benchmarked against conventional machine learning and deep learning approaches.<n>Within this framework, the Large Language Model (LLM) serves as the central reasoning module, performing bothtemporal activity recognition and explainable decision analysis from video data.<n> Experimental results demonstrate that the proposed LLM-based approach outperforms baseline models, achieving an improvement of approximately 15-20% in both accuracy and F1 score.
  arXiv  Detail & Related papers  (2026-01-29T14:46:48Z)
• RAICL: Retrieval-Augmented In-Context Learning for Vision-Language-Model Based EEG Seizure Detection [12.189806103703887]
  We propose a paradigm shift from conventional signal-based decoding by leveraging large-scale vision-language models (VLMs) to analyze EEG waveform plots.<n>To address the inherent non-stationarity of EEG signals, we introduce a Retrieval-Augmented In-Context Learning (RAICL) approach.
  arXiv  Detail & Related papers  (2026-01-25T13:58:31Z)
• MMedExpert-R1: Strengthening Multimodal Medical Reasoning via Domain-Specific Adaptation and Clinical Guideline Reinforcement [63.82954136824963]
  Medical Vision-Language Models excel at perception tasks with complex clinical reasoning required in real-world scenarios.<n>We propose a novel reasoning MedVLM that addresses these challenges through domain-specific adaptation and guideline reinforcement.
  arXiv  Detail & Related papers  (2026-01-16T02:32:07Z)
• E^2-LLM: Bridging Neural Signals and Interpretable Affective Analysis [54.763420895859035]
  We present ELLM2-EEG-to-Emotion Large Language Model, first MLLM framework for interpretable emotion analysis from EEG.<n>ELLM integrates a pretrained EEG encoder with Q-based LLMs through learnable projection layers, employing a multi-stage training pipeline.<n>Experiments on the dataset across seven emotion categories demonstrate that ELLM2-EEG-to-Emotion Large Language Model achieves excellent performance on emotion classification.
  arXiv  Detail & Related papers  (2026-01-11T13:21:20Z)
• MedAlign: A Synergistic Framework of Multimodal Preference Optimization and Federated Meta-Cognitive Reasoning [52.064286116035134]
  We develop MedAlign, a framework to ensure visually accurate LVLM responses for Medical Visual Question Answering (Med-VQA)<n>We first propose a multimodal Direct Preference Optimization (mDPO) objective to align preference learning with visual context.<n>We then design a Retrieval-Aware Mixture-of-Experts (RA-MoE) architecture that utilizes image and text similarity to route queries to a specialized and context-augmented LVLM.
  arXiv  Detail & Related papers  (2025-10-24T02:11:05Z)
• Retrieval-Augmented Framework for LLM-Based Clinical Decision Support [0.19999259391104388]
  This paper proposes a clinical decision support system powered by Large Language Models (LLMs) to assist prescribing clinicians.<n>The framework integrates natural language processing with structured clinical inputs to produce contextually relevant recommendations.<n>We outline the system's technical components, including representation representation alignment and generation strategies.
  arXiv  Detail & Related papers  (2025-10-01T18:45:25Z)
• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• CANDLE: A Cross-Modal Agentic Knowledge Distillation Framework for Interpretable Sarcopenia Diagnosis [3.0245458192729466]
  CANDLE mitigates the interpretability-performance trade-off, enhances predictive accuracy, and preserves high decision consistency.<n>The framework offers a scalable approach to knowledge assetization of TML models, enabling interpretable, reproducible, and clinically aligned decision support in sarcopenia and potentially broader medical domains.
  arXiv  Detail & Related papers  (2025-07-26T15:50:08Z)
• Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models [52.2001050216955]
  Existing methods aim to enhance the performance of Medical Vision Language Model (MedVLM) by adjusting model structure, fine-tuning with high-quality data, or through preference fine-tuning.<n>We propose an expert-in-the-loop framework named Expert-Controlled-Free Guidance (Expert-CFG) to align MedVLM with clinical expertise without additional training.
  arXiv  Detail & Related papers  (2025-07-12T09:03:30Z)
• Test-Time-Scaling for Zero-Shot Diagnosis with Visual-Language Reasoning [37.37330596550283]
  We introduce a framework for reliable medical image diagnosis using vision-language models.<n>A test-time scaling strategy consolidates multiple candidate outputs into a reliable final diagnosis.<n>We evaluate our approach across various medical imaging modalities.
  arXiv  Detail & Related papers  (2025-06-11T22:23:38Z)
• An Explainable Diagnostic Framework for Neurodegenerative Dementias via Reinforcement-Optimized LLM Reasoning [1.5646349560044959]
  We propose a framework that integrates two core components to enhance diagnostic transparency.<n>First, we introduce a modular pipeline for converting 3D T1-weighted brain MRIs into textual radiology reports.<n>Second, we explore the potential of modern Large Language Models (LLMs) to assist clinicians in the differential diagnosis.
  arXiv  Detail & Related papers  (2025-05-26T13:18:32Z)
• PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing [49.243031514520794]
  Large Language Models (LLMs) excel at capturing long-range signals due to their text-centric design.<n>PhysLLM achieves state-the-art accuracy and robustness, demonstrating superior generalization across lighting variations and motion scenarios.
  arXiv  Detail & Related papers  (2025-05-06T15:18:38Z)
• Proof-of-TBI -- Fine-Tuned Vision Language Model Consortium and OpenAI-o3 Reasoning LLM-Based Medical Diagnosis Support System for Mild Traumatic Brain Injury (TBI) Prediction [1.1488411226515398]
  We propose Proof-of-TBI, a medical diagnosis support system that integrates vision-language models with the OpenAI-o3 reasoning large language model (LLM)<n>Our approach fine-tunes multiple vision-language models using a labeled dataset of TBI MRI scans, training them to diagnose TBI symptoms effectively.<n>The system evaluates the predictions from all fine-tuned vision language models using the OpenAI-o3 reasoning LLM, a model that has demonstrated remarkable reasoning performance.
  arXiv  Detail & Related papers  (2025-04-25T19:49:30Z)
• Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
  Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions. VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
  arXiv  Detail & Related papers  (2024-05-29T23:19:28Z)
• XAI4LLM. Let Machine Learning Models and LLMs Collaborate for Enhanced In-Context Learning in Healthcare [16.79952669254101]
  We introduce a knowledge-guided in-context learning framework to enable large language models to process structured clinical data.<n>Our approach integrates domain-specific feature groupings, carefully balanced few-shot examples, and task-specific prompting strategies.
  arXiv  Detail & Related papers  (2024-05-10T06:52:44Z)
• Empowering Healthcare through Privacy-Preserving MRI Analysis [3.6394715554048234]
  We introduce the Ensemble-Based Federated Learning (EBFL) Framework. EBFL framework deviates from the conventional approach by emphasizing model features over sharing sensitive patient data. We have achieved remarkable precision in the classification of brain tumors, including glioma, meningioma, pituitary, and non-tumor instances.
  arXiv  Detail & Related papers  (2024-03-14T19:51:18Z)
• AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
  We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs. This setup allows for realistic assessments of LLMs in clinical scenarios. We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
  arXiv  Detail & Related papers  (2024-02-15T06:46:48Z)

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.