What does this research mean for the field?

Artificial intelligence assistance significantly improves clinicians' diagnostic accuracy of craniosynostosis from skull radiographs across varying levels of experience. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To assess whether artificial intelligence assistance improves the accuracy of clinicians interpreting skull radiographs for craniosynostosis.

August 23, 2026Scientific ReportsOpen Access

Effect of artificial intelligence assistance on skull radiograph diagnosis of craniosynostosis

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Authors

TKTaehoon Kim DKDong Yeong Kim SKSeung-Ki Kim

Discussion

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Overview

Multireader multicase paired study reveals AI assistance improves craniosynostosis detection from 75.0% to 84.7% on skull radiographs, suggesting reduced diagnostic variability across clinicians.

Key Points

To assess whether artificial intelligence assistance improves the accuracy of clinicians interpreting skull radiographs for craniosynostosis.
Conducted a multireader, multicase paired study in which 23 clinicians evaluated 59 independent skull radiograph cases both unassisted and with AI assistance.
Applied a Dorfman-Berbaum-Metz (DBM) jackknife multireader multicase analysis to assess diagnostic performance while accounting for reader and case variability.
AI assistance significantly increased overall clinician diagnostic accuracy from 75.0% to 84.7% (difference, 0.097; 95% CI, 0.053–0.141; p = 0.00015).
The standalone AI model achieved a cross-validation mean AUROC of 0.975, with AI assistance narrowing the performance gap between clinicians and the model.
Accuracy gains were most pronounced among neurosurgery residents and significantly enhanced the reliability of high-confidence diagnostic decisions.

Cite This Study

Kim et al. (2026) studied this question.

synapsesocial.com/papers/6a8aad667677a34114445807 https://doi.org/10.1038/s41598-026-67399-9

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August 23, 2026Scientific ReportsOpen Access

Effect of artificial intelligence assistance on skull radiograph diagnosis of craniosynostosis

View Full Paper

Ask AI

Bookmark

Authors

TKTaehoon Kim DKDong Yeong Kim SKSeung-Ki Kim

Discussion

Member takes

Overview

Multireader multicase paired study reveals AI assistance improves craniosynostosis detection from 75.0% to 84.7% on skull radiographs, suggesting reduced diagnostic variability across clinicians.

Key Points

To assess whether artificial intelligence assistance improves the accuracy of clinicians interpreting skull radiographs for craniosynostosis.
Conducted a multireader, multicase paired study in which 23 clinicians evaluated 59 independent skull radiograph cases both unassisted and with AI assistance.
Applied a Dorfman-Berbaum-Metz (DBM) jackknife multireader multicase analysis to assess diagnostic performance while accounting for reader and case variability.
AI assistance significantly increased overall clinician diagnostic accuracy from 75.0% to 84.7% (difference, 0.097; 95% CI, 0.053–0.141; p = 0.00015).
The standalone AI model achieved a cross-validation mean AUROC of 0.975, with AI assistance narrowing the performance gap between clinicians and the model.
Accuracy gains were most pronounced among neurosurgery residents and significantly enhanced the reliability of high-confidence diagnostic decisions.

Cite This Study

Kim et al. (2026) studied this question.

synapsesocial.com/papers/6a8aad667677a34114445807 https://doi.org/10.1038/s41598-026-67399-9

View Full Paper

Ask AI

Bookmark