Un marco de adherencia de reporte para estudios odontológicos de inteligencia artificial: un análisis automatizado de minería de texto

Ana Rodríguez Yanes and Anarodriguez@infomed.sld.cu

SAP Artificial Intelligence in Dentistry, 2025

Abstract: Introduction: Reporting guidelines for artificial intelligence (CLAIM, TRIPOD+AI, STARD-AI) exist but their uptake in dental artificial-intelligence research is unquantified. Scattered assessments in other specialties suggest methodological items are under-reported. Methods: Open-access dental artificial-intelligence diagnostic and prognostic studies were retrieved from Europe PMC (2013-2025). The items of CLAIM, TRIPOD+AI and STARD-AI were operationalized into a framework of 22 items across five domains (data, model, validation, performance, transparency), and full texts were mined automatically for each item. Results: The framework was applied to 296 studies. Mean study adherence was 73.1% (SD 15.0). Adherence was highest in the data (87.6%) and performance (83.8%) domains and lowest in the model (57.6%) and validation (59.5%) domains. Reporting of the area under the curve reached 99.3%, whereas code availability (33.1%) and external validation (34.1%) were the least reported items. Adherence increased weakly over time (Spearman rho 0.11, p 0.049). Conclusions: Performance metrics are reported almost universally in dental artificial-intelligence studies, but methodological and transparency items lag substantially. The five-domain framework provides a reusable, automated instrument for auditing reporting completeness in the field.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:cwf:aidart:aid202542

DOI: 10.62486/aid202542

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