Background/Objectives: 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. Methods: 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. Results: 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. Conclusions: 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.

Background/Objectives: Spinal excitability may undergo adaptive modulation in response to training load, sport-specific demands, and fatigue. While high-impact sports are known to influence reflex responsiveness, the extent to which these changes differ from athletes in lower-impact disciplines remains unclear. This study aimed to investigate post-exercise changes in Hmax/Mmax ratio among trained runners with varied sport backgrounds, and to identify emergent physiological profiles that may reflect differential spinal adaptation to fatigue. Methods: Twenty-two trained athletes underwent unilateral H-reflex testing before and after treadmill running performed to voluntary exhaustion. Amplitudes of the H-reflex and M-wave were recorded, and Hmax/Mmax ratios were analyzed. Based on a physiologically relevant threshold commonly used to distinguish normal from suppressed reflex amplitudes, participants were post hoc classified into three groups: Group A (pre- and post-test ratios above threshold), Group B (pre above, post below), and Group C (both below). A two-way repeated-measures ANOVA was used to assess between-group effects. Results: Significant differences were found across groups and conditions (p < 0.001). Group A maintained reflex ratios above the threshold, indicating stable excitability. Group B showed the greatest suppression (approximately 66%), transitioning from normal to subthreshold values. Group C remained consistently below-threshold. A significant interaction (p < 0.0001) confirmed that reflex modulation varied by physiological profile. A small but statistically significant reduction in H-reflex latency was also observed; however, this change remained within normal physiological variability. Conclusions: Postexercise H-reflex modulation revealed heterogeneous neuromuscular responses among athletes. These findings may contribute to understanding how sport-specific demands and fatigue shape spinal excitability and may help identify individuals with adaptive or potentially pathological profiles relevant to sports diagnostics.