
Clinical staff outperform ChatGPT AI at emergency department triage, but AI shows promise as a support tool for urgent cases.
Key Details
- 1Study compared six doctors, 44 nurses, and ChatGPT 3.5 on triaging 110 clinical cases using the Manchester Triage System.
- 2Doctors achieved 70.6% accuracy, nurses 65.5%, and AI 50.4%; AI had lower sensitivity for urgent cases (58.3% vs. nurses 73.8%, doctors 83%).
- 3AI outperformed nurses in the most urgent triage category for accuracy (27.3% vs. 9.3%) and specificity (27.8% vs. 8.3%).
- 4AI tended to over-triage, assigning higher urgency more often than staff.
- 5Authors advocate for AI as an adjunct to, not replacement for, clinical judgement.
- 6Study limitations include small sample size, single center, and non-real-world AI setting.
Why It Matters
These findings emphasize the current limitations of general AI models in patient triage compared to clinical professionals, highlighting the need for cautious integration, oversight, and further development before AI can play a leading role in acute medical decision-making.

Source
EurekAlert
Related News

•EurekAlert
AI Predicts Risks for Outpatient Stem Cell Therapy in Myeloma
Researchers use machine learning to predict adverse events during stem cell therapy for multiple myeloma, improving outpatient safety.

•EurekAlert
AI-Enhanced CT Heart Fat Measurement Boosts Cardiovascular Risk Prediction
AI-derived measurement of heart fat from CT scans significantly improves long-term cardiovascular disease risk prediction.

•EurekAlert
Molecular Test BiliSeq Greatly Improves Bile Duct Cancer Detection
The BiliSeq molecular test developed at UPMC doubled detection sensitivity for bile duct cancer compared to standard pathology.