SynthesisBMC medical education2024
Large language models for generating medical examinations: systematic review.
Synthesis in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 3 of them syntheses that pooled it.
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
56 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The performance of ChatGPT and other large language models on multiple-choice questions in biomedical disciplines: A meta-analysis.Anatomical sciences education · 2026Pooled it
- Pooled it
- Evaluating the Potential and Accuracy of ChatGPT-3.5 and 4.0 in Medical Licensing and In-Training Examinations: Systematic Review and Meta-Analysis.JMIR medical education · 2025Pooled it
- Psychometric performance and student perceptions of AI- versus student-generated multiple-choice questions: a single-center randomized controlled trial.BMC medical education · 2026Trial
- Evaluating AI-generated examination papers in periodontology: a comparative study with human-designed counterparts.BMC medical education · 2025Trial
- Performance of large language models in the Chinese National Medical Licensing Examinations and Beyond: Scoping review.PLOS digital health · 2026Article
- Can students identify AI? - A cross-sectional quantitative study about AI recognition in tablet-based MCQ assessment among fifth-year undergraduate medical students at Saarland University, Germany.BMC medical education · 2026Article
- Artificial intelligence in reproductive medicine and education: current evidence, challenges, and future directions.Journal of assisted reproduction and genetics · 2026Review
- Application of artificial intelligence-generated clinical cases in case-based learning of histology and embryology.Anatomical sciences education · 2026Article
- Large language model prompt engineering for medical education: A practical guide for the Australian and New Zealand College of Anaesthetists Final Examination.Anaesthesia and intensive care · 2026Article
- Examining the Role of Artificial Intelligence in Assessment: A Comparative Study of ChatGPT and Educator-Generated Multiple-Choice Questions in a Dental Exam.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026Article
- AI-Generated Microlearning for Plastic Surgery Residency: Single-Arm Pre-Post Feasibility Study.JMIR medical education · 2026Article
- Feasibility and psychometric evaluation of large language model-generated radiology learning materials for board examination preparation: a prospective pilot study.BMC medical education · 2026Observational
- Large language models in implant dentistry: a scoping review of applications, performance, and limitations.BMC oral health · 2026Article
- Evaluation of large language models in cardiovascular surgery: a comparative study of board-level clinical question answering and generation.Journal of cardiothoracic surgery · 2026Article
- Performance Evaluation of Large Language Models in Multilingual Medical Multiple-Choice Questions: Mixed Methods Study.JMIR medical education · 2026Article
- Fine-Tuned Large Language Models for Generating Multiple-Choice Questions in Anesthesiology: Psychometric Comparison With Faculty-Written Items.JMIR formative research · 2026Article
- Artificial intelligence in medical ethics education: a descriptive study of eight models in multiple choice question generation.BMC medical education · 2026Article
- Evaluating the ability of AI models to generate level-specific medical MCQs with variable difficulty.BMC research notes · 2026Article
- AI-Driven Objective Structured Clinical Examination Generation in Digital Health Education: Comparative Analysis of Three GPT-4o Configurations.JMIR medical education · 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
6 authors.
Funding
Abstract
backgroundWriting multiple choice questions (MCQs) for the purpose of medical exams is challenging. It requires extensive medical knowledge, time and effort from medical educators. This systematic review focuses on the application of large language models (LLMs) in generating medical MCQs.
methodsThe authors searched for studies published up to November 2023. Search terms focused on LLMs generated MCQs for medical examinations. Non-English, out of year range and studies not focusing on AI generated multiple-choice questions were excluded. MEDLINE was used as a search database. Risk of bias was evaluated using a tailored QUADAS-2 tool.
resultsOverall, eight studies published between April 2023 and October 2023 were included. Six studies used Chat-GPT 3.5, while two employed GPT 4. Five studies showed that LLMs can produce competent questions valid for medical exams. Three studies used LLMs to write medical questions but did not evaluate the validity of the questions. One study conducted a comparative analysis of different models. One other study compared LLM-generated questions with those written by humans. All studies presented faulty questions that were deemed inappropriate for medical exams. Some questions required additional modifications in order to qualify.
conclusionsLLMs can be used to write MCQs for medical examinations. However, their limitations cannot be ignored. Further study in this field is essential and more conclusive evidence is needed. Until then, LLMs may serve as a supplementary tool for writing medical examinations. 2 studies were at high risk of bias. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
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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.