Potential of large language models for rapid clinical information support: evidence from acute kidney injury knowledge testing

Abstract Large language models (LLMs) are increasingly applied in clinical contexts; however, to our knowledge, their performance in acute kidney injury (AKI) has not been directly compared with physician performance on standardized clinical knowledge assessments. We conducted a cross-sectional study to evaluate the performance of 13 publicly available LLMs and 123 volunteer participants at the 131st Annual Congress of the German Society of Internal Medicine in Wiesbaden, Germany. Both groups completed an identical AKI knowledge assessment consisting of two case vignettes and 15 single-best-answer multiple-choice questions. LLMs achieved a mean score of 13.5 out of 15 (90%), with several models reaching a perfect score, while human participants averaged 7.3 out of 15 (48.7%). Only 16.3% of participants scored 11 points or higher. As an illustrative example, ChatGPT-4o completed the test in approximately 0.5 minutes, whereas humans required a mean of 7.3 minutes. These findings demonstrate that LLMs substantially outperformed a heterogeneous group of medical professionals in AKI knowledge assessments and did so with markedly greater efficiency. While this highlights the potential of LLMs as rapid, cost-effective tools for clinical knowledge support, their role in real-world patient care remains undetermined, and human clinical judgment remains essential to ensure safe, context-sensitive, and patient-centered care.