arXiv:2605.04221v2 Announce Type: replace Abstract: Clinical named entity recognition from dental progress notes is challenging because documentation is highly unstructured, domain-specific, and often privacy-sensitive. We developed a locally deployable framework that enables small language models to self-generate, verify, refine, and evaluate entity-specific prompts for extracting multiple clinical entities from dental notes. Using 1,200 annotated notes, we evaluated candidate open-weight models with multi-prompt ensemble inference and further adapted selected models using QLoRA-based supervi
The proliferation of sensitive data and the increasing capabilities of smaller AI models are converging, making privacy-preserving local AI solutions critical.
This development addresses a key hurdle for AI adoption in highly regulated sectors by enabling robust data extraction without relying on external, less secure large language models.
Healthcare providers can now leverage advanced AI for clinical data analysis on premises, significantly improving data security and operational independence.
- · Healthcare providers
- · Small language model developers
- · Privacy-focused AI solutions
- · AI-in-medicine sector
- · Cloud-based LLM providers (for sensitive data tasks)
- · Traditional manual data extraction services
Increased adoption of AI in private data environments like healthcare and finance.
Development of specialized small language models tailored for various niche, privacy-sensitive industries.
Potential for a competitive landscape where local, privacy-centric AI solutions gain significant market share over general-purpose cloud LLMs in regulated domains.
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Read at arXiv cs.CL