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

Abstract

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.