When used for veterinary triage, artificial intelligence models recognise emergencies but are more likely than veterinary staff to flag non‐urgent cases as urgent

Background This study assesses the capability of ChatGPT and nurses in accurately triaging emergency patients compared to veterinarians. Methods Retrospective observational study of canine patients that presented at a private veterinary specialist and emergency hospital. Given clinical signs and history, patients were assigned to a triage category (waiting times of 0, 15, 30‒60, 120 and 240 minutes). Triages were performed by three veterinarians, two nurses, ChatGPT‐3.5 and ChatGPT‐4.0. Statistical analysis was used to assess how often triage by ChatGPT and nurses agreed with veterinarians. Results ChatGPT has high sensitivity in identifying severe emergencies, correctly prioritising 80%‒90% of critical cases, but over‐triaged around 60% of non‐urgent cases as requiring immediate attention. ChatGPT's triage performance was comparable to that of nurses. When ChatGPT was used as a tool to flag severe cases (‘0 minutes’) in concert with nurses, triage sensitivity rose to 95%. Limitations The small sample of nurses limits the ability to assess how performance relative to artificial intelligence (AI) may vary with nurses’ triage experience. Conclusions AI models can be an effective tool for flagging severe cases and complementing nurse triages. However, the tendency to flag non‐urgent cases as requiring immediate attention may lead to increased pressure on emergency clinics.