ArticleEpilepsia2025
Can the large language model ChatGPT-4omni predict outcomes in adult patients with status epilepticus?
Article in Epilepsia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Modern generative large language models in epilepsy care: a scoping review of current applications, challenges, and future directions.Acta epileptologica · 2026Review
- Comparative performance of LLMs and machine learning in predicting complications after percutaneous kyphoplasty for osteoporotic vertebral compression fractures.NPJ digital medicine · 2026Article
- Large language models for predicting one-year major adverse cardiovascular events in acute coronary syndrome.iScience · 2026Article
- Traditional Cox regression outperforms large language models in predicting long-term progression of intermediate to advanced hepatocellular carcinoma.Frontiers in oncology · 2026Article
- The impact of prompting on ChatGPT's adherence to status epilepticus treatment guidelines.Scientific reports · 2025Article
- Can the large language model ChatGPT-4omni predict outcomes in adult patients with status epilepticus?Epilepsia · 2025Article
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Abstract
objectiveLarge language models (LLMs) have recently gained attention for clinical decision-making and diagnosis. This study evaluates the performance of the recently updated LLM Chat Generative Pre-Trained Transformer-4omni (ChatGPT-4o) in predicting clinical outcomes in patients with status epilepticus and compares its prognostic performance to the Status Epilepticus Severity Score (STESS).
methodsThis retrospective single-center cohort study was performed at the University Hospital Basel (tertiary academic medical center) from January 2005 to December 2022. It included consecutive adult patients (≥18 years of age) with a diagnosis of status epilepticus. The primary outcome was survival at hospital discharge, and the secondary outcome was return to premorbid neurological function at hospital discharge. The performance characteristics of ChatGPT4-o (sensitivity, specificity, Youden Index) were evaluated and compared to those of the STESS.
resultsOf 760 patients, 689 patients (90.7%) survived to discharge, and 317 survivors (41.7%) regained their premorbid neurological function at discharge. ChatGPT-4o predicted survival in 567 of 760 patients (74.6%), of which 45 died. ChatGPT-4o predicted death in 193 of 760 patients (25.4%), of which 167 survived, resulting in a sensitivity of 75.8% and a specificity of 36.6% (Youden Index 0.12, 95% confidence interval [CI] 0-.28) for predicting survival. ChatGPT-4o predicted return to premorbid neurologic function in 249 of 760 patients (32.8%), of which 112 did not return to their premorbid neurological function. ChatGPT-4o predicted no return to premorbid function in 511 of 760 patients (67.2%), of which 180 returned to their premorbid function, resulting in a sensitivity of 43.2% and a specificity of 74.7% (Youden Index .12, 95% CI .08-.28) for predicting return to premorbid neurological function. There was no difference in the prognostic performance of ChatGPT-4o and the STESS. A second round of prompting did not increase the predictive performance of ChatGPT-4o. SIGNIFICANCE: ChatGPT-4o unreliably predicts outcomes in patients with status epilepticus. Clinicians should refrain from using ChatGPT-4o for prognostication in these patients.
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