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

<b>Background/Objectives:</b> Accurate assessment of neuromuscular reflexes, such as the Hoffmann reflex (H-reflex), plays a critical role in sports science, rehabilitation, and clinical neurology. Conventional interpretation of H-reflex electromyography (EMG) waveforms is subject to inter-rater variability and interpretive bias, limiting reliability and standardization. This study aims to develop an automated, interpretable, and robust agentic AI–driven framework for H-reflex waveform analysis. <b>Methods:</b> We propose a fine-tuned Vision–Language Model (VLM) consortium combined with a reasoning Large Language Model (LLM)–enabled decision support system for automated H-reflex interpretation. Multiple VLMs were fine-tuned on curated datasets of H-reflex EMG waveform images annotated with expert clinical observations, recovery timelines, and athlete metadata. The VLM outputs were aggregated using a consensus-based strategy and further refined by a specialized reasoning LLM to ensure coherent, transparent, and explainable diagnostic assessments. Model fine-tuning employed Low-Rank Adaptation (LoRA) and 4-bit quantization to enable efficient deployment on consumer-grade hardware. <b>Results:</b> Experimental evaluation demonstrated that the proposed hybrid system delivers accurate, consistent, and clinically interpretable assessments of neuromuscular states, including fatigue, injury, and recovery, directly from EMG waveform images and contextual metadata. Compared with baseline models, the fine-tuned VLM consortium exhibited substantially improved precision, consistency, and contextual awareness, while the reasoning LLM enhanced diagnostic coherence through cross-model consensus and structured reasoning, thereby supporting responsible and explainable AI-driven decision making. <b>Conclusions:</b> This work presents, to the authors’ knowledge, the first integration of a responsible and explainable AI-driven decision support system for H-reflex analysis. The proposed framework advances the automation and standardization of neuromuscular diagnostics and establishes a foundation for next-generation AI-assisted decision support systems in sports performance monitoring, rehabilitation, and clinical neurophysiology.