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This paper is in the following e-collection/theme issue:

Natural Language Processing (1340) Clinical Informatics (2399) Clinical Information and Decision Making (3904) Methods and Instruments in Medical Informatics (437) Ontologies, Classifications, and Coding (472) Artificial Intelligence (5044) Ambient AI Scribes and AI-Driven Documentation Technologies (115)

Published on in Vol 14 (2026)

This is a member publication of Bodleian Libraries (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/94241, first published .
Doctor in lab coat using a tablet in a bright clinic

Clinical Context Variables Collectively Rival Model Choice in Embedding-Based Retrieval: Multi-Corpus Benchmark Study

Clinical Context Variables Collectively Rival Model Choice in Embedding-Based Retrieval: Multi-Corpus Benchmark Study

Authors of this article:

Yngve Mikkelsen1 Author Orcid Image
Article Authors Cited by (4) Tweetations (1) Metrics

Journals

  1. Mikkelsen Y. Effects of Model Choice, Corpus Context, and Post Hoc Correction on Layer-Level Embedding Degradation in Clinical Document Retrieval: Experimental Study. JMIR Medical Informatics 2026;14:e99639 View
  2. Enayati M, Priya V, Prochaska E, Sobe K, Wolfien M. From Fine-Tuning to Grounding: Retrieval-Augmented Generation for Biomedical LLMs in Research and Clinical Data Infrastructures. Sci 2026;8(9):266 View
  3. Mikkelsen Y. Deployment-Specific Benefit and Harm in Clinical Artificial Intelligence: Decision-Analytic Derivation and Multi-Institutional Evaluation of a Deployment Threshold (Preprint). JMIR AI 2026 View
  4. Mikkelsen Y. Cross-Site Stability of Embedding Model Rankings for Known-Item Retrieval From Clinical Notes: A Two-Corpus Comparative Evaluation Study (Preprint). JMIR AI 2026 View