ArticleEpilepsia open2025
Comparative assessment of artificial intelligence chatbots' performance in responding to healthcare professionals' and caregivers' questions about Dravet syndrome.
Article in Epilepsia open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 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
- Performance of ChatGPT-4o in Providing Information on Pediatric Inborn Errors of Immunity: A Cross-Sectional Evaluation.Journal of clinical medicine · 2026Article
- Can Large Artificial Intelligence-Based Linguistic Models Help to Obtain Information About Burning Mouth Syndrome?Oral diseases · 2026Article
- Comparative assessment of artificial intelligence chatbots' performance in responding to healthcare professionals' and caregivers' questions about Dravet syndrome.Epilepsia open · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveArtificial intelligence chatbots have been a game changer in healthcare, providing immediate, round-the-clock assistance. However, their accuracy across specific medical domains remains under-evaluated. Dravet syndrome remains one of the most challenging epileptic encephalopathies, with new data continuously emerging in the literature. This study aims to evaluate and compare the performance of ChatGPT 3.5 and Perplexity in responding to questions about Dravet Syndrome.
methodsWe curated 96 questions about Dravet syndrome, 43 from healthcare professionals and 53 from caregivers. Two epileptologists independently graded the chatbots' responses, with a third senior epileptologist resolving any disagreements to reach a final consensus. Accuracy and completeness of correct answers were rated on predefined 3-point scales. Incorrect responses were prompted for self-correction and re-evaluated. Readability was assessed using Flesch reading ease and Flesch-Kincaid grade level.
resultsBoth chatbots had the majority of their responses rated as "correct" (ChatGPT 3.5: 66.7%, Perplexity: 81.3%), with no significant difference in performance between the two (χ SIGNIFICANCE: Our findings underscore the potential of AI chatbots in delivering accurate and complete responses to Dravet syndrome queries. However, they have limitations, particularly in complex areas like treatment. Continuous efforts to update information and improve readability are essential. PLAIN LANGUAGE SUMMARY: Artificial intelligence chatbots have the potential to improve access to medical information, including on conditions like Dravet syndrome, but the quality of this information is still unclear. In this study, ChatGPT 3.5 and Perplexity correctly answered most questions from healthcare professionals and caregivers, with ChatGPT 3.5 performing better for caregivers. Treatment-related questions had the most incorrect answers, particularly those from healthcare professionals. Both chatbots demonstrated the ability to correct previous incorrect responses, particularly Perplexity. Both chatbots produced text requiring advanced reading skills. Further improvements are needed to make the text easier to understand and address difficult medical topics.
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