Healthcare resource utilization for the management of neonatal head shape deformities: a propensity-matched analysis of AI-assisted and conventional approaches

As neurosurgeons and pediatricians continue to work to limit neonatal radiation exposure and contain healthcare costs, AI-assisted clinical care could be a cheap and easily scalable diagnostic adjunct for reducing reliance on radiography and encouraging adherence to established clinical guidelines. In practice, however, providers appear to default to preexisting diagnostic biases and underweight AI-generated data and interpretations, ultimately negating any potential advantages offered by AI. AI engineers and specialty leadership should prioritize provider education and user interface optimization to improve future adoption of validated AI diagnostic tools.

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Publication Details

Journal
Journal of Neurosurgery Pediatrics
Published
2025-03-29
DOI
https://doi.org/10.3171/2024.12.peds24429
Citations
1
Primary Topic
Craniofacial Disorders and Treatments
Type
article
Field-Weighted Citation Impact
1.82
Controls
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article

Healthcare resource utilization for the management of neonatal head shape deformities: a propensity-matched analysis of AI-assisted and conventional approaches

Petronella Stoltz, Markus J. Bookland, Jonathan E. Martin, David S. Hersh et al.
1 citations
Journal of Neurosurgery Pediatrics
Craniofacial Disorders and Treatments
1.82
article

Healthcare resource utilization for the management of neonatal head shape deformities: a propensity-matched analysis of AI-assisted and conventional approaches

Petronella Stoltz, Markus J. Bookland, Jonathan E. Martin, David S. Hersh, Gabriel Caron, Jimin Shin
article en
1 citations

Abstract

As neurosurgeons and pediatricians continue to work to limit neonatal radiation exposure and contain healthcare costs, AI-assisted clinical care could be a cheap and easily scalable diagnostic adjunct for reducing reliance on radiography and encouraging adherence to established clinical guidelines. In practice, however, providers appear to default to preexisting diagnostic biases and underweight AI-generated data and interpretations, ultimately negating any potential advantages offered by AI. AI engineers and specialty leadership should prioritize provider education and user interface optimization to improve future adoption of validated AI diagnostic tools.

Journal of Neurosurgery PediatricsVol. 35(6)
University of Connecticut (US), University of Hartford (US), Connecticut Children's Medical Center (US), Hartford Financial Services (United States)
Openalex Percentile: Top 17%
Craniofacial Disorders and Treatments
1.82
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