The Rise of Small Language Models in Healthcare: A Comprehensive Survey
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917998546124800 |
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| author | Garg, Muskan Raza, Shaina Rayana, Shebuti Liu, Xingyi Sohn, Sunghwan |
| author_facet | Garg, Muskan Raza, Shaina Rayana, Shebuti Liu, Xingyi Sohn, Sunghwan |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_17119 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Rise of Small Language Models in Healthcare: A Comprehensive Survey Garg, Muskan Raza, Shaina Rayana, Shebuti Liu, Xingyi Sohn, Sunghwan Computation and Language Artificial Intelligence 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 |
| title | The Rise of Small Language Models in Healthcare: A Comprehensive Survey |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2504.17119 |