ArticleJournal of the American Medical Informatics Association : JAMIA2024
Disparities in seizure outcomes revealed by large language models.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Applications of Natural Language Processing and Large Language Models for Social Determinants of Health: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Evaluating and addressing demographic disparities in medical large language models: a systematic review.International journal for equity in health · 2025Pooled it
- Review
- Zero-Shot Extraction of Seizure Outcomes from Clinical Notes Using Generative Pretrained Transformers.Journal of healthcare informatics research · 2025Article
- Extracting epilepsy-related information from unstructured clinic letters using large language models.Epilepsia · 2025Article
- Inductive reasoning with large language models: A simulated randomized controlled trial for epilepsy.Epilepsy research · 2025Article
- What can you do with a large language model?Journal of the American Medical Informatics Association : JAMIA · 2024Article
Corrections and comments
- Update of
Authors and funding
11 authors.
Funding
Abstract
objectiveLarge-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
methodsWe 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-classified seizure freedom at each office visit to test for demographic outcome disparities, using univariable and multivariable analyses.
resultsWe analyzed 84 675 clinic visits from 25 612 unique patients seen at our epilepsy center. We found little evidence of bias in the prediction accuracy or confidence of outcome classifications across demographic groups. Multivariable analysis indicated worse seizure outcomes for female patients (OR 1.33, P ≤ .001), those with public insurance (OR 1.53, P ≤ .001), and those from lower-income zip codes (OR ≥1.22, P ≤ .007). Black patients had worse outcomes than White patients in univariable but not multivariable analysis (OR 1.03, P = .66).
conclusionWe 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.