Letter to the Editor: "Evaluating the efficacy of large language models in predicting intensive care unit admission needs".

This letter comments on the study by Turan et al., which evaluates the efficacy of large language models (LLMs) in predicting ICU admission needs. While commending the rigorous benchmark established for off-the-shelf LLMs, we propose methodological directions to shift the evaluation paradigm from validating performance against local decisions toward demonstrating generalizable clinical value. Key suggestions include: aligning validation with patient-centered outcomes and dynamic risk prediction; enhancing transparency and reproducibility for evaluating LLMs on unstructured data; exploring ensemble and privacy-preserving multi-center frameworks to improve robustness and generalizability; and prioritizing research on human-AI collaborative decision-making. These reflections aim to guide the development of reliable, interpretable, and clinically integrated intelligent aids.