Large Language Models for Dental Radiology Report Generation From Structured Textual Data (DENT-LLM)
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.
Visão geral do estudo
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Intervenção / Tratamento
Descrição detalhada
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.
Tipo de estudo
Inscrição (Estimado)
Contactos e Locais
Contato de estudo
- Nome: Kamila Chęcińska, dr inż.
- Número de telefone: +48 694 816 344
- E-mail: kamila.checinska@pimmswia.gov.pl
Locais de estudo
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Świętokrzyskie Voivodeship
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Kielce, Świętokrzyskie Voivodeship, Polônia, 25-375
- Department of Maxillofacial Surgery
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Contato:
- Maciej Sikora, dr hab.
- Número de telefone: +48 41 260 55 85
- E-mail: maciej.sikora@pimmswia.gov.pl
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Critérios de participação
Critérios de elegibilidade
Idades elegíveis para estudo
- Adulto
- Adulto mais velho
Aceita Voluntários Saudáveis
Método de amostragem
População do estudo
Descrição
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
Plano de estudo
Como o estudo é projetado?
Detalhes do projeto
Coortes e Intervenções
Grupo / Coorte |
Intervenção / Tratamento |
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Dental radiology records
Structured dental radiology records used to evaluate large language model-assisted generation of narrative dental radiology reports.
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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.
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O que o estudo está medindo?
Medidas de resultados primários
Medida de resultado |
Descrição da medida |
Prazo |
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Factual consistency of large language model-generated dental radiology reports with structured source data
Prazo: At the time of report generation and expert evaluation, up to 12 months
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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.
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At the time of report generation and expert evaluation, up to 12 months
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Medidas de resultados secundários
Medida de resultado |
Descrição da medida |
Prazo |
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Completeness of large language model-generated dental radiology reports
Prazo: At the time of report generation and expert evaluation, up to 12 months
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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.
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At the time of report generation and expert evaluation, up to 12 months
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Error rate and error categories in large language model-generated dental radiology reports
Prazo: At the time of report generation and expert evaluation, up to 12 months
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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.
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At the time of report generation and expert evaluation, up to 12 months
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Overall quality score of dental radiology reports
Prazo: At the time of blinded or non-blinded expert evaluation, up to 12 months
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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.
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At the time of blinded or non-blinded expert evaluation, up to 12 months
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Difference in expert-rated quality between traditional and large language model-assisted dental radiology reports
Prazo: At the time of comparative expert evaluation, up to 12 months
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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.
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At the time of comparative expert evaluation, up to 12 months
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Effect of prompt design and model parameters on generated report quality
Prazo: At the time of prompt and parameter comparison, up to 12 months
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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.
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At the time of prompt and parameter comparison, up to 12 months
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Usability of the large language model-assisted dental radiology reporting workflow
Prazo: At the time of usability assessment, up to 12 months
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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.
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At the time of usability assessment, up to 12 months
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- CT/2026/1
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