A Voice Processing System For Orthodontic Records

This study aimed to develop an automated system that transforms clinical orthodontic conversations into standardized medical records, serving as an auxiliary tool allowing clinicians to dedicate more time to patient care. We constructed a modular voice processing framework with several integrated components including speech signal optimization algorithms, speech recognition technology, text segmentation processes, prompt engineering techniques, and structured data extraction modules. Our system utilized natural language processing (NLP) techniques to automatically identify and extract clinically relevant information from conversational dialogues. We then conducted comparative analyses across multiple AI models to evaluate information extraction accuracy. Performance metrics were collected for each model to determine which models performed better and were more suitable for future clinical use. The framework successfully converted clinical voice data into standardized orthodontic records. Under the same input conditions, notable performance variations were observed among different AI models. The developed system offers a promising approach for the transformation of spoken clinical communications into standardized orthodontic documentation. The automated system may improve overall documentation accuracy and consistency across different doctors, potentially leading to better clinical outcomes.