ArticleAnatomical sciences education2025
AI's ability to interpret unlabeled anatomy images and supplement educational research as an AI rater.
Article in Anatomical sciences education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline 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
- Comparative evaluation of radiological anatomy knowledge and accuracy of ChatGPT-5, Gemini 2.5, and Grok 4 across normal and thinking modes.Anatomical sciences education · 2026Article
- Review
- Comparison of artificial intelligence and lecturers' grading in the evaluation of handwritten open-ended anatomy examinations.Surgical and radiologic anatomy : SRA · 2026Article
- Is artificial intelligence getting better at anatomy? A two-year review of ChatGPT's free public versions.Anatomical sciences education · 2026Article
- Performance of multimodal large language models on image-based surgical anatomy, anatomical pathology, and radiology questions.Anatomical sciences education · 2026Article
- Dissecting interprofessional anatomy education: insights from AI-driven assessment.BMC medical education · 2026Article
- Comparative Morphometric and Structural Analysis of the Ovine Brain: Integrating Traditional Anatomical Methods with Artificial Intelligence-Driven 3D Modeling and Identification.Veterinary sciences · 2026Article
- Article
- Critical evaluation of large language models for human cross-sectional anatomy identification: implications for collaborative intelligence.BMC research notes · 2026Article
- AI's ability to interpret unlabeled anatomy images and supplement educational research as an AI rater.Anatomical sciences education · 2025Article
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Evidence suggests custom chatbots are superior to commercial generative artificial intelligence (GenAI) systems for text-based anatomy content inquiries. This study evaluates ChatGPT-4o's and Claude 3.5 Sonnet's capabilities to interpret unlabeled anatomical images. Secondarily, ChatGPT o1-preview was evaluated as an AI rater to grade AI-generated outputs using a rubric and was compared against human raters. Anatomical images (five musculoskeletal, five thoracic) representing diverse image-based media (e.g., illustrations, photographs, MRI) were annotated with identification markers (e.g., arrows, circles) and uploaded to each GenAI system for interpretation. Forty-five prompts (i.e., 15 first-order, 15 second-order, and 15 third-order questions) with associated images were submitted to both GenAI systems across two timepoints. Responses were graded by anatomy experts for factual accuracy and superfluity (the presence of excessive wording) on a three-point Likert scale. ChatGPT o1-preview was tested for agreement against human anatomy experts to determine its usefulness as an AI rater. Statistical analyses included inter-rater agreement, hierarchical linear modeling, and test-retest reliability. ChatGPT-4o's factual accuracy score across 45 outputs was 68.0% compared to Claude 3.5 Sonnet's score of 61.5% (p = 0.319). As an AI rater, ChatGPT o1-preview showed moderate to substantial agreement with human raters (Cohen's kappa = 0.545-0.755) for evaluating factual accuracy according to a rubric of textbook answers. Further improvements and evaluations are needed before commercial GenAI systems can be used as credible student resources in anatomy education. Similarly, ChatGPT o1-preview demonstrates promise as an AI assistant for educational research, though further investigation is warranted.
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