<scp>AI</scp> language model applications for early diagnosis of childhood epilepsy based on unstructured first‐visit patient narratives: A cohort study

Both models demonstrated relatively good performance in diagnosing childhood epilepsy solely based on the first-visit patient anamnesis text. Notably, the more advanced sentence-embedding model showed no improvement over the computationally simpler Naïve Bayes model. This suggests that modeling of anamnesis data does depend on word order for this particular classification task. Further refinement and exploration of language models and computational linguistic approaches are necessary to enhance diagnostic accuracy in clinical practice.