The Rise of Small Language Models in Healthcare: A Comprehensive Survey
Muskan Garg, Shaina Raza, Shebuti Rayana, Xingyi Liu, Sunghwan Sohn
TL;DR
The paper tackles the challenge of deploying AI in healthcare under privacy, latency, and resource constraints by surveying small language models (SLMs) and proposing a comprehensive taxonomy. It covers architectural foundations (data, tokenization, attention), adaptation strategies (prompting, in-context learning, fine-tuning), and compression techniques (distillation, pruning, quantization), along with current-state benchmarks and safety assessments. Key contributions include a proposed taxonomy for tuning and compression, a timeline of healthcare SLMs, a curated open-resource inventory, and sustainability metrics such as carbon footprints to guide practical deployment. The work highlights the potential for on-device, privacy-preserving CDSS and clinician-friendly AI tools, while outlining critical challenges—benchmarking, data access, bias, and regulatory alignment—that must be addressed to realize real-world impact.
Abstract
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