ArticleCommunications medicine2026
Evaluating the accuracy of ChatGPT model versions for giving care-seeking advice.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Patient-Facing AI System for Symptom Guidance Using Simulated Encounters and Physician Review: Analytical Validation Study.JMIR formative research · 2026Article
- Artificial Intelligence Tools in Pre-Travel Health Consultations: A Scoping Review of Clinical Evidence, Implementation Gaps, and Emerging Opportunities.Tropical medicine and infectious disease · 2026Review
- Increasing Large Language Model Accuracy for Care-Seeking Advice Using Prompts Reflecting Human Reasoning Strategies in the Real World: Validation Study.JMIR biomedical engineering · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial Intelligence tools such as ChatGPT are increasingly used by laypeople to support their care-seeking decisions, although the accuracy of newer models remains unclear. We aimed to evaluate the accuracy of care-seeking advice that is generated by all currently available ChatGPT models.
methodsWe evaluated 22 ChatGPT models using 45 validated vignettes, each prompted ten times (9,900 total assessments). Each model classified the vignettes as requiring emergency care, non-emergency care, or self-care. We evaluated accuracy against each case's gold standard solution (determined by two physicians), examined the variability across trials, and tested algorithms to aggregate multiple recommendations to improve accuracy.
resultsWe show that o1-mini achieves the highest accuracy (74%), but we cannot observe an overall improvement with newer models - although reasoning models (e.g., o4-mini) improved their accuracy in identifying self-care cases. Selecting the lowest urgency level across multiple trials improves accuracy by 4 percentage points.
conclusionsAlthough newer increasingly provide self-care advice, their accuracy remains insufficient for standalone use. However, making use of output variability with aggregation algorithms can improve the performance of existing models.
Identifiers
What OpenQuestion holds
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.