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Large Language Models for Dental Radiology Report Generation From Structured Textual Data
Sponsor: Hospital of the Ministry of Interior, Kielce, Poland
Summary
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.
Official title: Evaluation of Large Language Models for Transforming Structured Dental Radiology Data Into Narrative Radiology Reports
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
100
Start Date
2026-06-29
Completion Date
2026-07-26
Last Updated
2026-06-30
Healthy Volunteers
No
Interventions
Large language model-assisted radiology report generation
Structured dental radiology data will be processed using a large language model to generate narrative dental radiology reports. The model will transform predefined structured findings into report text for research evaluation. The model will not independently interpret radiographic images, make clinical diagnoses, recommend treatment, or replace professional review. Generated reports will be assessed for completeness, factual consistency with the source data, clarity, terminology, errors, safety, and potential workflow usefulness.
Locations (1)
Department of Maxillofacial Surgery
Kielce, Świętokrzyskie Voivodeship, Poland