Large Language Models for Dental Radiology Report Generation From Structured Textual Data (DENT-LLM)

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.