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.
The authors found little evidence that our LLM was intrinsically biased against any demographic group. Seizure freedom extracted by LLM revealed disparities in seizure outcomes across several demographic groups. These findings quantify the critical need to reduce disparities in the care of people with epilepsy.
Author(s):
Xie K., et al
References including authors:
Xie K, Ojemann WKS, Gallagher RS, Shinohara RT, Lucas A, Hill CE, Hamilton RH, Johnson KB, Roth D, Litt B, Ellis CA. Disparities in seizure outcomes revealed by large language models. J Am Med Inform Assoc. 2024 May 20;31(6):1348-1355. doi: 10.1093/jamia/ocae047. PMID: 38481027; PMCID: PMC11105138.