The purpose of this observational methodological study is to evaluate whether large language models can transform structured dental radiology data into clear narrative radiology reports. Large language models are computer programs that can generate text from information provided to them. In this study, the input will consist of organized dental radiology findings, such as chart-style or diagram-based information about teeth and surrounding structures.
Dental radiology reports are used by dentists and other health care providers to understand imaging findings and support clinical documentation. Preparing narrative reports may be time-consuming, and the wording of reports may vary between clinicians. This study will examine whether language-model-assisted report generation can produce reports that are complete, accurate, understandable, and clinically useful.
The study will compare reports generated with support from large language models with traditionally prepared reports. Researchers will also assess how the wording of the prompt and selected model parameters influence report quality. In addition, the study will analyze errors and safety risks in generated reports and evaluate whether such a system could be practical in a dental radiology workflow. The language model will not make treatment decisions, and generated reports will be used for research evaluation only.
Dental radiology records based on dental X-ray examination performed on the basis of a written referral from a dentist or physician
Dental X-ray examinations performed for screening, diagnostic, or treatment-planning purposes
Records from patients with permanent dentition after completion of exfoliation
Records from patients with mixed dentition before completion of exfoliation
Records with incomplete, ambiguous, or internally inconsistent structured dental radiology data preventing reliable report generation
Records with missing information required for evaluation of the generated report
Duplicate records from the same radiographic examination