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Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
Introduction to Language of Pathophysiology l01:25

Introduction to Language of Pathophysiology l

Pathophysiology investigates how biological mechanisms—typically starting at the cellular level—disrupt normal bodily functions. It bridges anatomy and physiology to explain the progression of disease. With this foundation, it is important to understand the following key terms used to describe disease processes: Diagnosis:The process of identifying a disease using clinical evaluation, including signs (objective evidence like rashes), symptoms (subjective experiences like pain), laboratory test...
Introduction to Language of Pathophysiology ll01:17

Introduction to Language of Pathophysiology ll

This lesson explores key terms that describe how diseases progress, their outcomes, and their distribution in populations.Diagnostic tests identify diseases and monitor treatment. These include blood and urine tests, biopsies, imaging (X-ray, MRI), and detection of infectious agents.Remission is a reduction or disappearance of symptoms.Exacerbation refers to the worsening of symptoms, such as increased wheezing during an asthma attack.A precipitating factor triggers an acute episode, while a...

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Updated: Jun 2, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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The Rise of Small Language Models in Healthcare: A Comprehensive Survey.

Muskan Garg1, Shaina Raza2, Shebuti Rayana3

  • 1Artificial Intelligence & Informatics, Mayo Clinic, USA.

Computer Science Review
|June 1, 2026
PubMed
Summary
This summary is machine-generated.

Small language models (SLMs) provide a scalable solution for healthcare informatics, addressing data privacy and resource limitations. This survey categorizes SLMs, offering a framework for their development and application in clinical settings.

Keywords:
carbon emission reductionhealthcare informaticsmental health analysissmall language models

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Last Updated: Jun 2, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

  • * Medical Informatics
  • * Natural Language Processing
  • * Artificial Intelligence

Background:

  • * Large language models (LLMs) show promise in healthcare but face challenges with data privacy and resource demands.
  • * Small language models (SLMs) offer a viable alternative for resource-constrained healthcare environments.
  • * Next-generation healthcare informatics requires efficient and scalable language model solutions.

Purpose of the Study:

  • * To present a comprehensive survey and taxonomic framework for healthcare small language models (SLMs).
  • * To analyze the timeline and contributions of SLMs in healthcare across NLP tasks, stakeholder roles, and the continuum of care.
  • * To provide healthcare professionals and informaticians with resources for SLM research and development.

Main Methods:

  • * Development of a taxonomic framework to categorize healthcare SLMs.
  • * Analysis of SLM architectural foundations, adaptation techniques (prompting, fine-tuning, reasoning), and optimization strategies (compression).
  • * Compilation of experimental results for various healthcare NLP tasks to demonstrate SLM capabilities.

Main Results:

  • * A structured taxonomy for identifying and categorizing healthcare SLMs.
  • * Insights into adapting SLMs for clinical precision and ensuring their accessibility and sustainability.
  • * Experimental evidence showcasing the effectiveness of SLMs across diverse healthcare NLP tasks.

Conclusions:

  • * Small language models (SLMs) present a scalable and clinically relevant solution for healthcare informatics.
  • * The provided framework and resources can guide future research and development in healthcare SLMs.
  • * SLMs hold transformative potential for advancing healthcare applications in resource-constrained settings.