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The Rise of Small Language Models in Healthcare: A Comprehensive Survey

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arxiv 2504.17119 v2 pith:H4BZMTAZ submitted 2025-04-23 cs.CL cs.AI

Muskan Garg , Shaina Raza , Shebuti Rayana , Xingyi Liu , Sunghwan Sohn This is my paper · ORCID
classification cs.CLcs.AI
keywords healthcaremodelscomprehensiveslmsframeworklanguagesurveyacross
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Despite substantial progress in healthcare applications driven by large language models (LLMs), growing concerns around data privacy, and limited resources; the small language models (SLMs) offer a scalable and clinically viable solution for efficient performance in resource-constrained environments for next-generation healthcare informatics. Our comprehensive survey presents a taxonomic framework to identify and categorize them for healthcare professionals and informaticians. The timeline of healthcare SLM contributions establishes a foundational framework for analyzing models across three dimensions: NLP tasks, stakeholder roles, and the continuum of care. We present a taxonomic framework to identify the architectural foundations for building models from scratch; adapting SLMs to clinical precision through prompting, instruction fine-tuning, and reasoning; and accessibility and sustainability through compression techniques. Our primary objective is to offer a comprehensive survey for healthcare professionals, introducing recent innovations in model optimization and equipping them with curated resources to support future research and development in the field. Aiming to showcase the groundbreaking advancements in SLMs for healthcare, we present a comprehensive compilation of experimental results across widely studied NLP tasks in healthcare to highlight the transformative potential of SLMs in healthcare. The updated repository is available at Github

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. In-Context Learning for Wound Classification with Small Multimodal Language Models

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Retrieval-based in-context learning, not zero-shot prompting, drives wound-classification gains in small multimodal models, with Qwen 3.5 27B reaching 0.872 accuracy on Kaggle and 0.678 on Medetec.

  2. Second Opinion Matters: Towards Adaptive Clinical AI via the Consensus of Expert Model Ensemble

    cs.AI 2025-05 conditional novelty 4.0 of 10

    An ensemble of expert medical LLMs with triage and weighted consensus reports accuracy gains over single frontier models on medical QA benchmarks.

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