ArticleThe ultrasound journal2026
SPOT THE BOT: A comparative quality assessment of AI-generated written instructions for lung ultrasound training.
Article in The ultrasound journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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Authors and funding
7 authors.
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Abstract
backgroundThe rapid proliferation of artificial intelligence (AI) in medical education has outpaced the development of quality assurance methods for AI-generated content. This study provides the first systematic evaluation of AI-generated instructional materials for lung ultrasound (LUS) training.
methodsThe ATLAS study employed a cross-sectional, multi-rater evaluation design comparing six instruction sources (five AI systems and human-generated content) across ten LUS content sessions. Expert evaluators (n=39) assessed materials using five standardized domains: Medical Accuracy, Evidence Completeness, Clarity, Practical Utility, and Pedagogical Quality. Statistical analysis included Kruskal-Wallis tests and pairwise comparisons with Bonferroni correction.
resultsSignificant differences existed between instruction sources (H = 92.582, p < 0.001). Manus AI achieved the highest overall rating (4.55±0.83) and significantly outperformed human instructions in Medical Accuracy (p = 0.0002) and Evidence Completeness (p < 0.001). Gemini AI (3.94±0.97) performed statistically equivalent to human instructions (4.23±1.00). ChatGPT (2.62±1.35) and Meta (1.53±1.02) performed significantly worse than human instructions (p < 0.001). Clarity emerged as the most discriminating criterion with the widest performance range (1.53-4.54).
conclusionsCertain AI systems can generate high-quality LUS instructional materials that match or exceed human-generated content. However, significant quality variations across AI systems emphasize the critical importance of systematic evaluation before implementation. These findings support cautious but optimistic integration of high-performing AI systems into medical education with appropriate quality assurance measures.
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Registered trials
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