Large Language Models for Dental Radiology Report Generation From Structured Textual Data
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorHospital of the Ministry of Interior, Kielce, Poland
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
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
Disqualifiers
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
Trial population
The study population will consist of dental radiology records from patients admitted to the radiology department in Kielce, a city in southern Poland with approximately 200,000 inhabitants. Eligible records will include dental X-ray examinations performed on the basis of a written referral from a dentist or physician, including examinations performed for screening, diagnostic, or treatment-planning purposes. The study will include records from patients with permanent dentition after completion of exfoliation, provided that structured dental radiology data are available for transformation into narrative radiology reports.
Trial design
Design model
Other
Time perspective
Retrospective
Treatments tested in this trial
Large language model-assisted radiology report generation
Other interventionStructured 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.
Treatment groups
100 Participants
are divided into 1 treatment groupGroup A: Dental radiology records1 intervention
Trial outcomes
Primary outcomes
1
Factual consistency of large language model-generated dental radiology reports with structured source data
Factual consistency will be assessed by comparing each large language model-generated narrative dental radiology report with the corresponding structured dental radiology source data. Expert evaluators will assess whether the generated report accurately reflects the source data without adding findings, omitting findings, changing tooth numbering, or altering the clinical meaning of the structured findings. The outcome will be reported as the proportion of generated reports without clinically relevant factual inconsistency and/or as the number and type of factual inconsistencies per report.
Time frame
At the time of report generation and expert evaluation, up to 12 monthsSecondary outcomes
1
Completeness of large language model-generated dental radiology reports
Completeness will be assessed by determining whether all predefined findings present in the structured dental radiology source data are included in the generated narrative report. The outcome will be reported as the proportion of required findings correctly included in each report and/or the proportion of complete reports.
Time frame
At the time of report generation and expert evaluation, up to 12 months2
Error rate and error categories in large language model-generated dental radiology reports
Generated reports will be reviewed for predefined error categories, including omitted findings, added findings not present in the source data, incorrect tooth numbering, inconsistent terminology, ambiguous wording, and statements with potential clinical relevance. The outcome will be reported as the number and frequency of each error category per report and across all generated reports.
Time frame
At the time of report generation and expert evaluation, up to 12 months3
Overall quality score of dental radiology reports
Overall report quality will be assessed by qualified evaluators using a predefined rating scale that may include clarity, readability, terminology, organization, completeness, and clinical usefulness. The outcome will be reported as the mean or median quality score for generated reports and, where applicable, for traditionally prepared reports.
Time frame
At the time of blinded or non-blinded expert evaluation, up to 12 months4
Difference in expert-rated quality between traditional and large language model-assisted dental radiology reports
Traditional narrative dental radiology reports and large language model-assisted reports will be compared using expert assessment. Evaluators may be blinded to the report-generation method where feasible. The outcome will be reported as the difference in predefined quality scores between traditional and model-assisted reports.
Time frame
At the time of comparative expert evaluation, up to 12 monthsSponsors and contacts
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Source ClinicalTrials.gov