Large language models for turning structured dental radiology data into narrative reports
Official title: Evaluation of Large Language Models for Transforming Structured Dental Radiology Data Into Narrative Radiology Reports
Run by Hospital of the Ministry of Interior, Kielce, Poland · NCT07676318 · Data updated 2026-11-12
This study tests whether a language model can convert organized dental X‑ray findings into clear, complete radiology reports.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 100 (estimated) |
| Sponsor | Hospital of the Ministry of Interior, Kielce, Poland Academic / other |
| Locations | 1 site (Kielce, Świętokrzyskie Voivodeship) |
| Status | Recruiting |
| Started | January 1, 2026 |
| Main results expected | November 2026 |
| Study ends | December 2026 |
| Registry record updated | September 22, 2026 |
| Trial ID | NCT07676318 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Dental radiology records from patients with permanent dentition after exfoliation, with structured radiology data suitable for report generation.
✕Probably not a fit
Patients whose records lack complete or reliable structured input data may not benefit from improved reporting.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This observational study evaluates the use of large language models to generate narrative dental radiology reports from structured input such as charts or diagrams. It focuses on the quality, clarity, and clinical usefulness of the reports, comparing model-generated outputs with traditionally prepared reports. The project examines how prompt design and model settings influence report quality, aiming to identify configurations that improve completeness and factual accuracy without interpreting images or making diagnoses. Data are restricted to structured radiology findings and do not involve autonomous diagnostic decisions. The research will assess reliability, terminology, readability, and alignment with source data across multiple configurations.
Why it matters
Potential benefit: If successful, clinicians may gain faster, consistent radiology reporting from structured data while preserving accuracy and readability.
How similar studies have gone: Prior work in other medical domains suggests language-model-assisted report generation can improve efficiency and consistency, though dental-specific validation is ongoing.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * 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 Exclusion Criteria: * 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
Where it's running
-
Department of Maxillofacial Surgery
Kielce, Świętokrzyskie Voivodeship, Poland Recruiting
Study contacts
- Study coordinator: Kamila Chęcińska, dr inż.
- Email: kamila.checinska@pimmswia.gov.pl
- Phone: +48 694 816 344
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.