  This research addresses the issue of missing structured data in dental
records by extracting diagnostic information from unstructured text. The
updated periodontology classification system's complexity has increased
incomplete or missing structured diagnoses. To tackle this, we use advanced AI
and NLP methods, leveraging GPT-4 to generate synthetic notes for fine-tuning a
RoBERTa model. This significantly enhances the model's ability to understand
medical and dental language. We evaluated the model using 120 randomly selected
clinical notes from two datasets, demonstrating its improved diagnostic
extraction accuracy. The results showed high accuracy in diagnosing periodontal
status, stage, and grade, with Site 1 scoring 0.99 and Site 2 scoring 0.98. In
the subtype category, Site 2 achieved perfect scores, outperforming Site 1.
This method enhances extraction accuracy and broadens its use across dental
contexts. The study underscores AI and NLP's transformative impact on
healthcare delivery and management. Integrating AI and NLP technologies
enhances documentation and simplifies administrative tasks by precisely
extracting complex clinical information. This approach effectively addresses
challenges in dental diagnostics. Using synthetic training data from LLMs
optimizes the training process, improving accuracy and efficiency in
identifying periodontal diagnoses from clinical notes. This innovative method
holds promise for broader healthcare applications, potentially improving
patient care quality.
