Large Language Models for Dental Radiology Report Generation From Structured Textual Data

NCT07676318 | Not Yet Recruiting

• 1 other identifier: CT/2026/1
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Radiography; Oral Health; Large Language Model; Natural Language Processing (NLP); Dentistry

Outcome Measures

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.

  At the time of report generation and expert evaluation, up to 12 months

Secondary Outcomes (6)

• Completeness of large language model-generated dental radiology reports

  At the time of report generation and expert evaluation, up to 12 months

• Error rate and error categories in large language model-generated dental radiology reports

  At the time of report generation and expert evaluation, up to 12 months

• Overall quality score of dental radiology reports

  At the time of blinded or non-blinded expert evaluation, up to 12 months

• Difference in expert-rated quality between traditional and large language model-assisted dental radiology reports

  At the time of comparative expert evaluation, up to 12 months

• Effect of prompt design and model parameters on generated report quality

  At the time of prompt and parameter comparison, up to 12 months

Study Arms (1)

Dental radiology records

Structured dental radiology records used to evaluate large language model-assisted generation of narrative dental radiology reports.

Other: Large language model-assisted radiology report generation

Interventions

Large language model-assisted radiology report generation OTHER

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.

Dental radiology records

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study 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.

You may qualify if:

• 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

You may not qualify if:

• 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
• Records in which anonymization or pseudonymization cannot be ensured

Sponsors & Collaborators

• Hospital of the Ministry of Interior, Kielce, Poland: lead

Study Sites (1)

Department of Maxillofacial Surgery

Kielce, Świętokrzyskie Voivodeship, 25-375, Poland

Location

Central Study Contacts

Kamila Chęcińska, dr inż.

CONTACT

+48 694 816 344; kamila.checinska@pimmswia.gov.pl

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 24, 2026
• First Posted: June 30, 2026
• Study Start: June 29, 2026
• Primary Completion: July 26, 2026
• Study Completion: July 26, 2026
• Last Updated: June 30, 2026

Record last verified: 2026-06

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ANALYTIC CODE
• Time Frame: Beginning at the time of publication of the main study results and available for at least 5 years.
• Access Criteria: The de-identified structured dental pathology notation dataset will be made available as supplementary material accompanying the publication of the main study results or through a scientific data repository. The shared data will not include radiographic images, direct identifiers, dates of birth, names, or other directly identifying information.

Locations

PL (1)