Completeness and Quality of Neurology Referral Letters Generated by a Large Language Model for Standardized Scenarios

Abstract

: ChatGPT produced neurology referral letters of high linguistic quality but variable completeness, especially for clinically complex content. The variability pattern among letters reflected model inconsistency rather than case type. The reliance on a single rater and use of a non-validated rubric represent limitations. Future studies should include multiple raters, inter-rater reliability testing, and validated scoring frameworks. Ultimately, access to tailored LLMs exclusively trained for medical documentation could improve outcomes while safeguarding patient privacy.

Ähnliche Arbeiten