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.
Authors
- Petronella Stoltz
- Markus J. Bookland (ORCID: https://orcid.org/0000-0003-4422-8376)
- Jonathan E. Martin (ORCID: https://orcid.org/0000-0003-3180-2084)
- David S. Hersh (ORCID: https://orcid.org/0000-0003-1177-2201)
- Gabriel Caron (ORCID: https://orcid.org/0000-0002-1993-1935)
- Jimin Shin
Institutions
- University of Connecticut (US)
- University of Hartford (US)
- Connecticut Children's Medical Center (US)
- Hartford Financial Services (United States)
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