Disparities in seizure outcomes revealed by large language models.

ABSTRACT:

Objective

Large-language models (LLMs) can potentially revolutionize health care delivery and research, but risk propagating existing biases or introducing new ones. In epilepsy, social determinants of health are associated with disparities in care access, but their impact on seizure outcomes among those with access remains unclear. Here we (1) evaluated our validated, epilepsy-specific LLM for intrinsic bias, and (2) used LLM-extracted seizure outcomes to determine if different demographic groups have different seizure outcomes.

Materials and methods

We tested our LLM for differences and equivalences in prediction accuracy and confidence across demographic groups defined by race, ethnicity, sex, income, and health insurance, using manually annotated notes. Next, we used LLM

SUBMITTER: Xie K 

PROVIDER: S-EPMC11105138 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature