ArticleComputer science review2026
The Rise of Small Language Models in Healthcare: A Comprehensive Survey.
Article in Computer science review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Extracting Medical Information From Unstructured Clinical Text Using Large Language Models to Enhance Health Care Interoperability: Proof-of-Concept Study.Journal of medical Internet research · 2026Article
- Large Language Models Accurately Identify People Who Inject Drugs From Infectious Diseases Discharge Summaries in an Australian Hospital.Drug and alcohol review · 2026Article
- Reinforcement learning improves LLM accuracy and reasoning in disease classification from radiology reports.NPJ digital medicine · 2026Article
- Performance Modeling of Lightweight Retrieval-Augmented Large Language Models for Low-Resource Plastic Surgery Settings.Bioengineering (Basel, Switzerland) · 2026Article
- AI assistance in tumor multidisciplinary teams.ESMO real world data and digital oncology · 2026Review
- Large Language Models in Cardiovascular Prevention: A Narrative Review and Governance Framework.Diagnostics (Basel, Switzerland) · 2026Review
- Can small language models handle context-summarized multi-turn customer-service QA? A synthetic data-driven comparative evaluation.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence in cardiovascular medicine: prevention, diagnosis, and intervention.Frontiers in artificial intelligence · 2026Review
- Combining Clinician Expertise with Prompt Engineering enhances Small Language Models Reliability for Cancer Entity Recognition in Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
- Leveraging reddit data for context-enhanced synthetic health data generation to identify low self esteem.Frontiers in psychiatry · 2025Article
- Privacy-, linguistic-, and information-preserving synthesis of clinical documentation through generative agents.Frontiers in artificial intelligence · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.