Privacy-Preserving Local Language Models for Longitudinal Data Retrieval in Chronic Dermatologic Disease: Implementation in Pemphigus Patients
A study evaluated the use of a privacy-preserving small language model (SLM) for retrieving clinical data in pemphigus patients. The model demonstrated an accuracy of 82.25% in feature retrieval and received high ratings from dermatologists for the quality and usefulness of its generated summaries. These findings suggest that SLMs can effectively support clinical decision-making in dermatology.
- ▪Thirty pemphigus patients contributed 541 visit notes for the study.
- ▪The locally deployed SLM achieved a mean accuracy of 82.25% across 1,680 feature retrieval tasks.
- ▪Dermatologists rated the AI-generated summaries highly for overall quality and clinical accuracy.
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Computer Science > Artificial Intelligence arXiv:2605.25020 (cs) [Submitted on 24 May 2026] Title:Privacy-Preserving Local Language Models for Longitudinal Data Retrieval in Chronic Dermatologic Disease: Implementation in Pemphigus Patients Authors:Abdurrahim Yilmaz, Ayşe Esra Koku Aksu, Duygu Yamen, Vefa Asli Erdemir, Mehmet Salih Gurel, Gulsum Gencoglan, Joram M. Posma, Burak Temelkuran View a PDF of the paper titled Privacy-Preserving Local Language Models for Longitudinal Data Retrieval in Chronic Dermatologic Disease: Implementation in Pemphigus Patients, by Abdurrahim Yilmaz and 7 other authors View PDF HTML (experimental) Abstract:Chronic dermatologic diseases such as pemphigus require long-term follow-up, generating extensive longitudinal clinical documentation that is difficult…
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