AI-assisted post-mortem classification of vehicle trauma in free-roaming cats using transformer-based large language models

Veterinary forensic science is a specialized discipline that investigates non-natural animal deaths and injuries. Determining the cause of death in free-roaming cats often presents challenges due to underdeveloped pattern recognition methods and the intricate interplay of events and contributing factors. The integration of natural language processing into forensic investigations may offer potential for the computerized automation of document analysis, with the goal of supporting investigational decision-making. The objective of this study was to develop an AI framework leveraging a transformer-based large language model (LLM) to classify veterinary forensic autopsy reports derived from 271 feline autopsies and to identify the causes of death. We first identified discriminative lexical patterns between vehicular trauma (VT; n = 111) and non-VT cases (n = 160) using Term Frequency-Inverse Document Frequency analysis. The best-performing model, based on Clinical-Longformer, achieved an accuracy of 0.945, precision of 0.885, recall of 1.000, F1 score of 0.939, and AUC-ROC of 0.950. Model interpretability using Integrated Gradients revealed that VT cases were characterized by terms associated with traumatic physical injuries, whereas non-VT cases exhibited a broader range of diagnostic descriptions. These findings demonstrate the feasibility of applying LLMs to veterinary forensic pathological texts and underscore the importance of domain-specific token interpretation for model explainability.