Assessing the readiness of dental electronic health records for machine learning prediction of procedure outcomes: Insights from the bigmouth repository on composite and amalgam restoration survival rates
Gaps in dental EHR data limit the accuracy of ML-based risk prediction. Enhancing diagnostic coding and data standardization can improve predictive modeling, support evidence-based decision-making, and optimize treatment planning. Strengthening EHR documentation will enable Artificial Intelligence-driven tools to enhance clinical outcomes and advance precision dentistry.
Authors
- Athanasios I. Zavras (ORCID: https://orcid.org/0000-0001-7622-1457)
- Jay S. Patel (ORCID: https://orcid.org/0000-0003-0559-5958)
- Hend Alqaderi (ORCID: https://orcid.org/0000-0003-3422-5091)
- Lauren Gritzer (ORCID: https://orcid.org/0000-0002-6648-914X)
- Mary Tavares (ORCID: https://orcid.org/0000-0003-4705-1238)
- Hesham Alhazmi
- Narjes Bencheikh (ORCID: https://orcid.org/0009-0006-7559-6103)
Institutions
- Boston University (US)
- Tufts University (US)
- University of California, San Francisco (US)
- Umm al-Qura University (SA)
- Dana-Farber/Harvard Cancer Center (US)
- Temple University (US)
Publication Details
- Journal
- Journal of Dentistry
- Published
- 2025-06-06
- DOI
- https://doi.org/10.1016/j.jdent.2025.105865
- Citations
- 2
- Primary Topic
- Dental Radiography and Imaging
- Type
- article
- Field-Weighted Citation Impact
- 3.58
Funders
- School of Dentistry, University of California, San Francisco