Purpose-built LLMs for dental note-taking

This post details the development of purpose-built LLMs for dental note-taking, focusing on three tasks: converting ambient transcripts to structured notes, real-time note updates, and note alteration. Key technical contributions include: 1. Development of a comprehensive evaluation framework (Lumina) with six binary evaluators to capture domain-specific failure modes (clinical terminology, contradictions, completeness, source fidelity, structural compliance, tooth notation). 2. Application of iterative Supervised Fine-Tuning (iSFT) for efficient model training, leveraging LLM-as-a-judge evaluations for dense, token-level supervision, resulting in 6-7x better sample efficiency than standard SFT. 3. A novel synthetic data generation approach to embed complex domain knowledge (e.g., four tooth notation systems) into models by transforming and rephrasing source text to increase diversity, generating over 50,000 training examples across translation, exam-style, scenario-based, and summarization tasks. The resulting models match `gemini-2.5-pro`'s accuracy on tooth notation and outperform it on real-time tasks, while responding 5-6x faster.