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Evaluating AI performance in nephrology triage and subspecialty referrals
Priscilla Koirala1, Charat Thongprayoon2, Jing Miao2
1Internal Medicine, Mayo Clinic, Rochester, MN, 55905, USA.
Scientific Reports
|January 27, 2025
Summary
Artificial intelligence (AI) shows promise in kidney care triage. ChatGPT accurately determined the need for nephrology consultations and subspecialty referrals in simulated cases.
Area of Science:
- Nephrology
- Artificial Intelligence
- Medical Triage
Background:
- Increasing prevalence of kidney diseases in aging populations necessitates efficient triage.
- Artificial intelligence (AI) offers potential solutions for optimizing medical workflows.
Purpose of the Study:
- To evaluate the utility of ChatGPT, a large language model, in triaging nephrology cases.
- To assess ChatGPT's accuracy in determining the appropriateness of nephrology consultations and subspecialty referrals.
Main Methods:
- Two nephrologists developed 100 simulated nephrology patient cases.
- ChatGPT's performance was evaluated on appropriateness of consultation and subspecialty triage in two rounds.
Main Results:
- ChatGPT demonstrated high accuracy, correctly identifying the need for nephrology in 99-100% of cases.
- Accurate subspecialty triage was achieved in 96-99% of cases, with 97% agreement between evaluation rounds.
Conclusions:
- AI, specifically ChatGPT, shows significant potential for enhancing medical triage efficiency and accuracy in nephrology.
- Further refinement is needed for integrating AI into multidisciplinary care for complex cases.
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