ArticleNeurology. Education2025
Education Research: Creating Online Interactive Case-Based Learning Experiences From Educational Case Reports With Large Language Models: A Feasibility Study.
Article in Neurology. Education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Education Research: Integrating AI-Enabled Interactive Case-Based Learning in a Preclinical Neurosciences Course.Neurology. Education · 2026Article
- Performance of large language models in urological decision support: a guideline-based comparative evaluation in urolithiasis.Urolithiasis · 2026Article
- Education Research: The Future of Child Neurology Residency Training: The Perspective of a Child Neurology Society Task Force.Neurology. Education · 2026Article
- Education Research: Quality of Narrative Feedback Generated by a Large Language Model Compared With Expert Faculty for Case-Based Learning in Neurology Education.Neurology. Education · 2026Article
- Automating Case-Based Learning in Obstetrics and Gynecology: Validation of a Locally Deployed Large Language Model.Journal of multidisciplinary healthcare · 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
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objectives: Case reports are a fundamental part of medical literature and education. Artificial intelligence (AI) is increasingly influencing medical education and can potentially augment the delivery of the educational content in case reports. The aim of this study was to evaluate the feasibility of using AI, namely large language models (LLMs), to convert previously published Methods: Three Results: The overall number of appropriate responses generated by the LLM was 206 of 210 (98.1%). There were 26 of 210 responses in which additional content was generated, all of which were medically plausible or consistent with the context of the case. The 4 errors that occurred were omissions of investigation results at the "screenplay" stage, which are amenable to manual correction. The omissions were the results of 3 unrevealing blood tests and 1 electroencephalogram result. None of these errors precluded the establishment of the diagnosis and completion of the case. Discussion: It is feasible to convert
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.