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A comparison of large language model-generated and published perioperative neurocognitive disorder recommendations: a
Sarah Saxena1, Odmara L Barreto Chang2, Melanie Suppan3
1Department of Surgery, Research Institute for Health Sciences and Technology, University of Mons, Mons, Belgium; Department of Anesthesiology, Helora, Mons, Belgium.
Background:
Perioperative neurocognitive disorders (PNDs) are common complications after surgery and anaesthesia, particularly in older adults, leading to increased morbidity, mortality, and healthcare costs. Therefore, major medical societies have developed recommendations for the prevention and treatment of PNDs. Our study evaluated the reliability of large language models, specifically ChatGPT-4 and Gemini, in generating recommendations for PND management and comparing them with published guidelines.
Methods:
We conducted an online cross-sectional web-based analysis over 48 h in June 2024. Artificial intelligence (AI)-generated recommendations were produced in six different locations across five countries (Switzerland, Belgium, Turkey, Canada, and the East and West Coasts of the USA). The English prompt 'a table of a bundle of care for perioperative neurocognitive disorders' was entered into ChatGPT-4 and Gemini, generating tables evaluated by independent reviewers. The primary outcomes were the Total Disagreement Score (TDS) and Quality Assessment of Medical Artificial Intelligence (QAMAI), which compared AI-generated recommendations with published guidelines.
Results:
The study generated 14 tables, with TDS and QAMAI scores showing similar results for ChatGPT-4 and Gemini (2 [1-3] vs 2 [2-3], P=0.636 and 4 [4-4] vs 4 [3-4], P=0.424, respectively). AI-generated recommendations aligned well with published guidelines, with the highest alignment observed in ChatGPT-4-generated recommendations. No complete agreement with guidelines was achieved, and lack of cited sources was a noted limitation.
Conclusions:
Large language models can generate perioperative neurocognitive disorder recommendations that align closely with published guidelines. However, further validation and integration of clinician feedback are required before clinical application.
