RadiographyDENT-LLMNCT07676318
Large Language Models for Dental Radiology Report Generation From Structured Textual Data
An observational study of Radiography and Oral Health, run by Hospital of the Ministry of Interior, Kielce, Poland at 1 site in Poland.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Radiography, Oral Health, Large Language Model
- What is being tested?
- Large language model-assisted radiology report generation
- How many people
- 100 participants
- Where
- 1 sitePoland
- Study dates
- 1 Jan 2026to Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Poland1 site, 1 recruiting
- Recruiting
Department of Maxillofacial Surgery
Kielce, Świętokrzyskie Voivodeship, 25-375
Maciej Sikora, dr hab., +48 41 260 55 85, maciej.sikora@pimmswia.gov.pl
About this study
Official title: Evaluation of Large Language Models for Transforming Structured Dental Radiology Data Into Narrative Radiology Reports
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.
Read the detailed description
This study is designed to evaluate the use of large language models for converting structured dental radiology data into narrative radiology reports. The project focuses on the quality, safety, and practical usability of language-model-assisted report generation in dental radiology.
Structured dental radiology data will be used as the input for the language model. These data may include organized findings recorded in a diagram, chart, or predefined structured format. The model will be asked to transform this structured information into a narrative report resembling a conventional dental radiology description. The study does not evaluate the model as an autonomous diagnostic system. The model will not independently interpret radiographic images, establish a diagnosis, or recommend treatment. Its role is limited to generating narrative text from already structured radiological information.
The study will include several related analyses. First, the investigators will assess whether a large language model can reliably transform structured dental radiology findings into a narrative report. Generated reports will be evaluated for completeness, factual consistency with the source data, clarity, terminology, and clinical readability.
Second, the study will examine how prompt construction and model parameters affect the quality of the generated reports. Different prompt formats and selected generation settings may be compared to identify configurations associated with higher report quality and fewer errors.
Third, reports generated with model assistance will be compared with traditionally prepared narrative reports. The comparison may include blinded assessment by qualified evaluators, who will judge report quality without knowing whether a report was generated traditionally or with model support.
Fourth, the study will include an error and safety analysis. Errors may include omitted findings, added findings not present in the source data, incorrect tooth numbering, inconsistent terminology, misleading wording, or statements that could affect clinical interpretation. The purpose of this analysis is to identify types of errors that may occur when large language models are used for this task and to assess their potential clinical relevance.
Finally, the study will assess the potential implementation usefulness of the report-generation workflow. This may include evaluation of usability, perceived time savings, acceptability to users, clarity of generated text, and the need for human review before clinical use.
All generated reports will require expert evaluation in the study setting. The system is intended to support documentation research and workflow assessment, not to replace professional judgment. The study will provide evidence on whether language-model-assisted transformation of structured dental radiology data into narrative reports is feasible, accurate, safe, and potentially useful for future clinical documentation workflows.
- Conditions
- Radiography, Oral Health, Large Language Model, Natural Language Processing (NLP), Dentistry
- Being tested
- Large language model-assisted radiology report generation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Large language model-assisted radiology report generationOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Dental radiology records | Structured dental radiology records used to evaluate large language model-assisted generation of narrative dental radiology reports. Receives: Other: Large language model-assisted radiology report generation |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
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.
Measured: 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
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.
Measured: At the time of report generation and expert evaluation, up to 12 months
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.
Measured: 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
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.
Measured: At the time of comparative expert evaluation, up to 12 months
Effect of prompt design and model parameters on generated report quality
The quality of reports generated using different prompt formats and selected model-generation parameters will be compared. Outcomes may include factual consistency, completeness, error rate, and overall quality score. The analysis will identify prompt and parameter configurations associated with higher report quality and fewer errors.
Measured: At the time of prompt and parameter comparison, up to 12 months
Usability of the large language model-assisted dental radiology reporting workflow
Usability will be assessed among users involved in evaluating or testing the model-assisted reporting workflow. Measures may include perceived usefulness, ease of use, clarity of generated reports, perceived need for editing, and potential workflow acceptability. The outcome will be reported using predefined questionnaire items or usability ratings.
Measured: At the time of usability assessment, up to 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Jun 2026
- Study start1 Jan 2026
- Primary completionNov 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for RadiographyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hospital of the Ministry of Interior, Kielce, Poland
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07676318
- Sponsor's protocol ID
- CT/2026/1
- Acronym
- DENT-LLM
- Status last verified
- Jun 2026
- First submitted
- 24 Jun 2026
- Last updated
- 22 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: The investigators plan to share a de-identified structured dataset containing symbolic dental pathology notation derived from panoramic dental radiographs, for example tooth-level coded entries such as "16DR", "15M", or "14C". The shared dataset will not include radiographic images, names, dates of birth, personal identifiers, or other directly identifying information. Data sharing will be performed in accordance with the approval and conditions specified by the Bioethics Committee. The dataset will be shared to support transparency, reproducibility, and independent verification of the large language model-assisted report generation task.
Study at a glance
From the registry record, last updated by the sponsor on 22 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of Large Language Models for Transforming Structured Dental Radiology Data Into Narrative Radiology Reports
- ClinicalTrials.gov ID
- NCT07676318
- Acronym / sponsor ID
- DENT-LLM / CT/2026/1
- Conditions
- Radiography, Oral Health, Large Language Model, Natural Language Processing (NLP), Dentistry
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Hospital of the Ministry of Interior, Kielce, PolandUniversity, hospital or other
- Study start
- 1 Jan 2026 (actual)
- Primary completion
- Nov 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 30 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
This study is designed to evaluate the use of large language models for converting structured dental radiology data into narrative radiology reports. The project focuses on the quality, safety, and practical usability of language-model-assisted report generation in dental radiology.
Structured dental radiology data will be used as the input for the language model. These data may include organized findings recorded in a diagram, chart, or predefined structured format. The model will be asked to transform this structured information into a narrative report resembling a conventional dental radiology description. The study does not evaluate the model as an autonomous diagnostic system. The model will not independently interpret radiographic images, establish a diagnosis, or recommend treatment. Its role is limited to generating narrative text from already structured radiological information.
The study will include several related analyses. First, the investigators will assess whether a large language model can reliably transform structured dental radiology findings into a narrative report. Generated reports will be evaluated for completeness, factual consistency with the source data, clarity, terminology, and clinical readability.
Second, the study will examine how prompt construction and model parameters affect the quality of the generated reports. Different prompt formats and selected generation settings may be compared to identify configurations associated with higher report quality and fewer errors.
Third, reports generated with model assistance will be compared with traditionally prepared narrative reports. The comparison may include blinded assessment by qualified evaluators, who will judge report quality without knowing whether a report was generated traditionally or with model support.
Fourth, the study will include an error and safety analysis. Errors may include omitted findings, added findings not present in the source data, incorrect tooth numbering, inconsistent terminology, misleading wording, or statements that could affect clinical interpretation. The purpose of this analysis is to identify types of errors that may occur when large language models are used for this task and to assess their potential clinical relevance.
Finally, the study will assess the potential implementation usefulness of the report-generation workflow. This may include evaluation of usability, perceived time savings, acceptability to users, clarity of generated text, and the need for human review before clinical use.
All generated reports will require expert evaluation in the study setting. The system is intended to support documentation research and workflow assessment, not to replace professional judgment. The study will provide evidence on whether language-model-assisted transformation of structured dental radiology data into narrative reports is feasible, accurate, safe, and potentially useful for future clinical documentation workflows.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Dental radiology records | – | Other: Large language model-assisted radiology report generationStructured dental radiology records used to evaluate large language model-assisted generation of narrative dental radiology reports. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Large language model-assisted radiology report generationStructured 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. | Other | Dental radiology records |
Primary outcome
Factual consistency of large language model-generated dental radiology reports with structured source data
Time frame: At the time of report generation and expert evaluation, up to 12 months
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.
6 secondary outcomes
Completeness of large language model-generated dental radiology reports
Time frame: 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
Time frame: At the time of report generation and expert evaluation, up to 12 months
Overall quality score of dental radiology reports
Time frame: 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
Time frame: At the time of comparative expert evaluation, up to 12 months
Effect of prompt design and model parameters on generated report quality
Time frame: At the time of prompt and parameter comparison, up to 12 months
Usability of the large language model-assisted dental radiology reporting workflow
Time frame: At the time of usability assessment, up to 12 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Sep 2026.
- Individual participant dataSharing planned: Yes. The investigators plan to share a de-identified structured dataset containing symbolic dental pathology notation derived from panoramic dental radiographs, for example tooth-level coded entries such as "16DR", "15M", or "14C". The shared dataset will not include radiographic images, names, dates of birth, personal identifiers, or other directly identifying information. Data sharing will be performed in accordance with the approval and conditions specified by the Bioethics Committee. The dataset will be shared to support transparency, reproducibility, and independent verification of the large language model-assisted report generation task.
Related trials for Radiography
RecruitingObservational study
Study of Sagittal Pelvic Statics in the Sitting Position in Children With Cerebral Palsy
Cerebral Palsy Infantile
Recruiting
A Multicenter Randomized Trial of CT-Based AI Decision Support for Early Pneumonia Care
Pneumonia; Pulmonary Infection
Recruiting
Detecting and Monitoring Scoliosis Without Radiography: Standard Radiographic Monitoring Program Versus Self-Screening With Momentum SpineTM
Adolescent Idiopathic Scoliosis
RecruitingPhase 3
Optimal Treatment Duration for Radiographically Apparent, Bacteriologically Unconfirmed TB, Identified Through Active Case Finding (RADIO-TB Trial)
Tuberculosis (TB)
RecruitingObservational study
A Study Evaluating the Performance of Using AI Software and Chest X-rays to Screen for Osteoporosis as Compared to Standard DXA Scans in Patients 50 Years and Older
Osteoporosis, Osteopenia
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Radiography and Oral Health, run by Hospital of the Ministry of Interior, Kielce, Poland at 1 site in Poland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 22 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Poland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Kamila Chęcińska, dr inż. (+48 694 816 344, kamila.checinska@pimmswia.gov.pl), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Factual consistency of large language model-generated dental radiology reports with structured source data (time frame: At the time of report generation and expert evaluation, up to 12 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Sep 2026.