ArticleFrontiers in medicine2026
Can artificial intelligence outperform experts in assessing clinical skills? Evidence from a comparative experiment.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Abstract
Background: This study evaluates and compares the performance of Artificial Intelligence Assessment (AIA) and Clinical Expert Assessment (CEA) in assessing clinical skills. Methods: Data were collected during two clinical skills competitions held in November 2024 and April 2025. Students' skills were assessed on history-taking, endotracheal intubation, and cardio-pulmonary resuscitation (CPR) by CEA and AIA at the same time. Statistical analyses included intraclass correlation coefficient (ICC), Pearson's correlation, paired-sample Results: For history-taking, endotracheal intubation, and CPR, scores of AIA and CEA showed moderate, excellent, and poor correlation, respectively. In history-taking, the results of ICC, Pearson's correlation, and paired-sample Discussion: The findings indicate that while AIA is good at objective structured content assessments, it struggles with subjective or context-rich scenarios. Conversely, CEA provides valuable holistic judgments but is likely to have bias and lack continuous attention. Combining both methods could leverage their respective strengths, improving evaluation reliability and educational outcomes. Conclusion: Integrating AIA for quantifying technical skills alongside CEA for comprehensive evaluation can enhance medical education assessments. Nonetheless, significant calibration and validation efforts are required before AI systems can be fully implemented.
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