TC3-VLM: A Vision-Language Model for Tactical Combat Casualty Care

Tactical Combat Casualty Care (TC3) defines the current standard of military trauma care in the pre-hospital and battlefield settings, where life-saving decisions are made while operating at the extremes of human performance, under high stress, and with limited information. Despite growing interest in AI-assisted battlefield medicine, visual reasoning remains underexplored in existing systems. Recent advances in vision-language models (VLMs) enable the integration of visual information with medical reasoning, but their application to combat casualty care remains limited. To address this gap, we present TC3-VLM, the first VLM explicitly tailored for combat casualty care. Using a dual question-answer (Q&A) generation framework, we create visual-grounded Q&A to capture spatial and situational reasoning alongside caption-based Q&A to encode protocol knowledge, resulting in a domain-specific dataset of 7,585 Q&A pairs derived from over 52 hours of TC3 videos. We then fine-tune multiple VLM backbones via Low-Rank Adaptation across varying model scales. Experiments demonstrate that fine-tuned models consistently outperform their baseline counterparts, with TC3-VLM surpassing a 72B-parameter general-purpose VLM despite being significantly smaller in scale. Ablation and a TC3-grounded error analysis indicate that visual grounding is the primary driver of these gains and that fine-tuning reduces safety-relevant errors, positioning TC3-VLM as a training and reference aid for combat casualty care.