Longitudinal assessment of an AI-based software for interproximal caries detection in bitewing radiographs
AI-based diagnostic tools evolve with data exposure, requiring clinicians to account for changing performance over time. While these systems can sharpen detection decisions from bitewing radiographs, successful use depends on continuous, longitudinal performance monitoring, tracking accuracy, and adjusting thresholds as needed.
Authors
- Guillermo Pradíes (ORCID: https://orcid.org/0000-0001-9716-1385)
- María Paz Salido (ORCID: https://orcid.org/0000-0001-6580-1835)
- Mónica Bonfanti‐Gris (ORCID: https://orcid.org/0000-0003-3399-6329)
- Shizhu Bai (ORCID: https://orcid.org/0000-0002-2439-3211)
- Natasha Batilieri Rodrigues (ORCID: https://orcid.org/0009-0007-2440-149X)
Institutions
- Universidad Complutense de Madrid (ES)
- Instituto Cajal (ES)
- Air Force Medical University (CN)
Publication Details
- Journal
- Journal of Dentistry
- Published
- 2025-11-20
- DOI
- https://doi.org/10.1016/j.jdent.2025.106252
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00