A framework for natural language processing of electronic dental records

Natural language processing has matured rapidly in medicine, yet its application to electronic dental records remains fragmented across terminology, extraction, prediction and evaluation studies that have not been assembled into a single account. This narrative review integrates those dispersed efforts into a five-component framework for natural language processing of electronic dental records. Sources were retrieved from Scopus, PubMed and Crossref and articulated around the components that emerged from the dental and medical informatics literature. The framework proceeds from data capture and standardization, through information extraction and phenotyping and prediction, to multimodal and generative integration and evaluation and deployment, and each component is defined and anchored to the evidence that supports it. A conceptual diagram places the components in sequence. The resulting framework differs from prior medical natural language processing models in that it foregrounds dental terminology and the tooth-centred structure of dental records, and it yields verifiable propositions about standardization, extraction performance and external validation. The evidence indicates that dental natural language processing is feasible and accurate for well-defined tasks but that external validation, public benchmarks and standardized dental terminologies remain the principal gaps.