arXiv:2606.13082v1 Announce Type: new Abstract: The extraction of structured clinical information from unstructured EHR notes is a persistent bottleneck in healthcare informatics. While large language models (LLMs) offer high performance, their deployment in clinical settings is hindered by privacy risks, inference costs, and the tendency to hallucinate beyond textual evidence. We address these challenges for the CL4Health 2026 Case Report Form (CRF) filling task by proposing a fully local, domain-adapted pipeline using the MedGemma-27B model. Our two-stage architecture, which separates binary
The increasing maturity of local LLMs and growing concerns over data privacy in healthcare are driving solutions that enable powerful AI without external dependencies.
This development allows healthcare providers to leverage advanced AI for critical tasks like CRF filling while adhering to strict privacy regulations and reducing operational costs and risks.
Healthcare institutions can now deploy powerful, domain-adapted LLMs for structured data extraction directly within their own infrastructure, reducing reliance on cloud-based solutions and mitigating privacy concerns.
- · Healthcare providers
- · Clinical research organizations
- · Patients (data privacy)
- · Local LLM developers
- · Cloud-based LLM providers (for sensitive data)
- · Manual data entry roles in healthcare
- · General-purpose, non-domain-adapted LLMs
More efficient and accurate extraction of clinical data for research and patient care using local LLMs.
Increased adoption of on-premise AI solutions in other privacy-sensitive industries, driven by regulatory compliance and cost considerations.
Potential for a competitive ecosystem of specialized, local LLMs tailored for various niche industry applications, shifting market power from general AI providers.
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Read at arXiv cs.CL