Performance of an artificial intelligence model for evaluation of unnecessary central lines, Northern California 2025.

We used a large language model integrated in the electronic health record to evaluate unnecessary central lines. It had a 16% sensitivity and 99% specificity for detecting unnecessary lines. Although it missed many unnecessary lines, the high specificity suggests potential as a tool where human review is not feasible.