ArticleBMC medical education2025
Is AI the future of evaluation in medical education?? AI vs. human evaluation in objective structured clinical examination.
Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Evaluating Large Language Model-Based Automated Scoring in a Voice-Based Virtual Standardized Patient Platform for Medical Students: Cross-Sectional Agreement Study.JMIR medical education · 2026Article
- Automated scoring of student videos in medical education: a comparison between a large language model and expert evaluation.Journal of microbiology & biology education · 2026Article
- AI for Clinical Competency Assessment: Scoping Review of Methods and Applications.JMIR medical education · 2026Article
- AI-Simulated Patients for Training Shared Decision-Making: Feasibility Study in Medical Education.JMIR medical education · 2026Article
- Global Insights and Key Trends in Objective Structured Clinical Examination Research Related to Residency Training: A Bibliometric Analysis.Journal of dental education · 2026Review
- Article
- Artificial intelligence-assisted feedback in pharmacology education: a pilot evaluation of a custom generative model.BMC medical education · 2026Article
- Evaluation of basic life support practice skills based on artificial intelligence technology: system construction and evaluation equivalence validation.BMC medical education · 2026Article
- Artificial intelligence analysis of facial movements and satisfaction in online medical lectures: a cross-sectional study.BMC medical education · 2026Article
- Assessing the educational quality of YouTube videos on celiac plexus blocks: Expert review and AI-based evaluation.Interventional pain medicine · 2026Article
- Towards accurate and interpretable competency-based assessment: enhancing clinical competency assessment through multimodal AI and anomaly detection.NPJ digital medicine · 2026Article
- AI-Driven Objective Structured Clinical Examination Generation in Digital Health Education: Comparative Analysis of Three GPT-4o Configurations.JMIR medical education · 2026Article
- Can artificial intelligence outperform experts in assessing clinical skills? Evidence from a comparative experiment.Frontiers in medicine · 2026Article
- Mapping applications and evaluations of LLM-enabled AI chatbots for health purposes: a scoping review.Frontiers in public health · 2026Article
- Artificial intelligence in undergraduate medical education clinical skills curricula: a scoping review of implementations since 2022.Frontiers in digital health · 2026Review
- Artificial intelligence vs. human evaluation of anesthesia education videos: a comparative analysis using validated quality scales.Frontiers in medicine · 2026Article
- Large language models for generating key-feature questions in medical education.Medical education online · 2025Article
- Evaluation of Few-Shot AI-Generated Feedback on Case Reports in Physical Therapy Education: Mixed Methods Study.JMIR medical education · 2025Article
- AI-Based Assessment of Non-Technical Skills in Prehospital Simulations: A Comparative Validation Study.Healthcare (Basel, Switzerland) · 2025Article
- Artificial intelligence-based chatbots improve the efficiency of course orientation among medical students: a cross-sectional study.BMC medical education · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundObjective Structured Clinical Examinations (OSCEs) are widely used in medical education to assess students' clinical and professional skills. Recent advancements in artificial intelligence (AI) offer opportunities to complement human evaluations. This study aims to explore the consistency between human and AI evaluators in assessing medical students' clinical skills during OSCE.
methodsThis cross-sectional study was conducted at a state university in Turkey, focusing on pre-clinical medical students (Years 1, 2, and 3). Four clinical skills-intramuscular injection, square knot tying, basic life support, and urinary catheterization-were evaluated during OSCE at the end of the 2023-2024 academic year. Video recordings of the students' performances were assessed by five evaluators: a real-time human assessor, two video-based expert human assessors, and two AI-based systems (ChatGPT-4o and Gemini Flash 1.5). The evaluations were based on standardized checklists validated by the university. Data were collected from 196 students, with sample sizes ranging from 43 to 58 for each skill. Consistency among evaluators was analyzed using statistical methods.
resultsAI models consistently assigned higher scores than human evaluators across all skills. For intramuscular injection, the mean total score given by AI was 28.23, while human evaluators averaged 25.25. For knot tying, AI scores averaged 16.07 versus 10.44 for humans. In basic life support, AI scores were 17.05 versus 16.48 for humans. For urinary catheterization, mean scores were similar (AI: 26.68; humans: 27.02), but showed considerable variance in individual criteria. Inter-rater consistency was higher for visually observable steps, while auditory tasks led to greater discrepancies between AI and human evaluators.
conclusionsAI shows promise as a supplemental tool for OSCE evaluation, especially for visually based clinical skills. However, its reliability varies depending on the perceptual demands of the skill being assessed. The higher and more uniform scores given by AI suggest potential for standardization, yet refinement is needed for accurate assessment of skills requiring verbal communication or auditory cues.
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.