Privacy-Preserving Local Language Models for Longitudinal Data Retrieval in Chronic Dermatologic Disease: Implementation in Pemphigus Patients
Researchers have developed a privacy-preserving small language model (SLM) capable of retrieving clinical features and generating longitudinal summaries from extensive patient records. In a study involving 30 pemphigus patients, the locally deployed Qwen3 4B Thinking 2507 model achieved 82.25% accuracy in feature retrieval and received high ratings from dermatologists for its generated summaries. The findings suggest that such SLMs, with appropriate oversight, can aid clinical decision-making by reducing clinician workload and improving the extraction of critical historical patient information. AI
IMPACT Demonstrates potential for LLMs to improve clinical efficiency and accuracy in managing complex patient histories.