ArticleJMIR medical education2024
Evaluating AI Competence in Specialized Medicine: Comparative Analysis of ChatGPT and Neurologists in a Neurology Specialist Examination in Spain.
Article in JMIR medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
12 citing papers in PubMed.
- Large language models as a screening tool for the qualification of patients with Parkinson's disease for device-aided therapies.Journal of neural transmission (Vienna, Austria : 1996) · 2026Article
- Performance of GPT-4o and Claude in Medical Ethics Scenarios: Comparative Study.JMIR medical education · 2026Article
- Comparison of the performance of ChatGPT-5, Gemini 3, Copilot, Perplexity, and medical students in answering neurology questions: a cross-sectional study.Scientific reports · 2026Article
- Artificial Intelligence in Cardiology Examination: A Comparative Study of Physicians, ChatGPT-4, and ChatGPT-5.Cureus · 2026Article
- From diagnostics to education: Multi-domain evaluation of LLM chatbots in neurology.Journal of Taibah University Medical Sciences · 2026Review
- When AI models take the exam: large language models vs medical students on multiple-choice course exams.Medical education online · 2025Article
- What's With All the Hype?: Applying Large Language Models in Neurology Education.Neurology. Education · 2025Article
- Article
- Large Language Models in Neurological Practice: Real-World Study.Journal of medical Internet research · 2025Article
- Evaluation of multiple generative large language models on neurology board-style questions.Frontiers in digital health · 2025Article
- Large language models for neurology: a mini review.Frontiers in digital health · 2025Review
- Large language models as clinical decision-support tools in multiple sclerosis and neuromyelitis optica spectrum disorders: A comparative study of ChatGPT-4o and neurologists.Multiple sclerosis journal - experimental, translational and clinicalArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: With the rapid advancement of artificial intelligence (AI) in various fields, evaluating its application in specialized medical contexts becomes crucial. ChatGPT, a large language model developed by OpenAI, has shown potential in diverse applications, including medicine. Objective: This study aims to compare the performance of ChatGPT with that of attending neurologists in a real neurology specialist examination conducted in the Valencian Community, Spain, assessing the AI's capabilities and limitations in medical knowledge. Methods: We conducted a comparative analysis using the 2022 neurology specialist examination results from 120 neurologists and responses generated by ChatGPT versions 3.5 and 4. The examination consisted of 80 multiple-choice questions, with a focus on clinical neurology and health legislation. Questions were classified according to Bloom's Taxonomy. Statistical analysis of performance, including the κ coefficient for response consistency, was performed. Results: Human participants exhibited a median score of 5.91 (IQR: 4.93-6.76), with 32 neurologists failing to pass. ChatGPT-3.5 ranked 116th out of 122, answering 54.5% of questions correctly (score 3.94). ChatGPT-4 showed marked improvement, ranking 17th with 81.8% of correct answers (score 7.57), surpassing several human specialists. No significant variations were observed in the performance on lower-order questions versus higher-order questions. Additionally, ChatGPT-4 demonstrated increased interrater reliability, as reflected by a higher κ coefficient of 0.73, compared to ChatGPT-3.5's coefficient of 0.69. Conclusions: This study underscores the evolving capabilities of AI in medical knowledge assessment, particularly in specialized fields. ChatGPT-4's performance, outperforming the median score of human participants in a rigorous neurology examination, represents a significant milestone in AI development, suggesting its potential as an effective tool in specialized medical education and assessment.
Indexed as
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.