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

Methods and Instruments in Medical Informatics (437) Clinical Informatics (2402) Clinical Information and Decision Making (3909) Decision Support for Health Professionals (2360) Generative Language Models Including ChatGPT (1648) AI Language Models in Health Care (839)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/99639, first published .
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Effects of Model Choice, Corpus Context, and Post Hoc Correction on Layer-Level Embedding Degradation in Clinical Document Retrieval: Experimental Study

Effects of Model Choice, Corpus Context, and Post Hoc Correction on Layer-Level Embedding Degradation in Clinical Document Retrieval: Experimental Study

Authors of this article:

Yngve Mikkelsen1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. 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
  2. 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