Nelson Advisors referenced in a Cornell University paper ‘The Rise of Small Language Models in Healthcare: A Comprehensive Survey’
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Nelson Advisors referenced in a Cornell University paper ‘The Rise of Small Language Models in Healthcare: A Comprehensive Survey’
1. Background
The global small language model (SLM) market is projected to reach USD 29.64 billion by 2032, with the healthcare industry anticipated to experience the fastest compound annual growth rate (CAGR) of 18.31% from 2024 to 20322.
This growth is driven by the growing adoption of SLM in medical diagnosis, patient care, and administrative processes. The use of SLM in analyzing extensive medical literature and patient data provides clinicians with evidence-based interpretations to support clinical decision making (3)
Source: https://arxiv.org/html/2504.17119v1
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 Github1.