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Scaling Responsible Medical Text Retrieval Across Silos: Evaluating Small Language Models with Retrieval-Augmented

Rahul Shetty¹, Karim Keshavjee¹

¹Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.

Studies in Health Technology and Informatics

|February 13, 2026

Abstract

None:

This study evaluates small language models with FAISS and Dynamic Top-k for diabetes-focused medical text retrieval, with all-mpnet-base-v2 performing best and FAISS (FAISS Facebook AI Similarity Search) maintaining sub-2 ms latency even at tenfold scale. Dynamic Top-k improved precision and nDCG (Normalized Discounted Cumulative Gain), showing that lightweight SLM-RAG (Small Language Model-Retrieval Augmented Generation) pipelines can approach larger-model accuracy while remaining highly scalable for cloud-based EMR (Electronic Medical Record) environments.

Keywords:

Digital Health Patient-Centered Innovation Responsible AI Retrieval-Augmented Generation Small Language Models

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