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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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.25020 |
| Publication time | Tue, 26 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-26T04:07:43.013Z |
| Last seen | 2026-05-26T04:07:43.013Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | sRe_OcFJd5HC |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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