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Issues And Trends In Healthcare Delivery System01:29

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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
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Small Language Models for Developing Agentic AI in Healthcare: A Comprehensive Systematic Review and Critical

Zain Khalpey1, Nicholas King2, Alyssa Abraham3

  • 1Department of Cardiothoracic Surgery, HonorHealth, Scottsdale, USA.

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|May 6, 2026
PubMed
Summary
This summary is machine-generated.

Small language models (SLMs) offer efficient and practical solutions for agentic artificial intelligence (AI) in healthcare. These models are suitable for tasks like documentation and triage, proving viable for clinical implementation.

Keywords:
agentic aiclinical decision supportcost-effectivenesshealthcare automationregulatory compliancesmall language models

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Area of Science:

  • Artificial Intelligence in Medicine
  • Health Informatics
  • Clinical Applications of AI

Background:

  • Agentic artificial intelligence (AI) systems are revolutionizing healthcare by enabling autonomous task execution.
  • Early AI healthcare applications primarily utilized large language models (LLMs).
  • Smaller language models (SLMs) present potential advantages in efficiency and scalability for clinical settings.

Purpose of the Study:

  • To review the current use of small language models (SLMs) in agentic healthcare applications.
  • To synthesize evidence on the performance, safety, and economic impact of SLMs in healthcare.
  • To discuss considerations for the clinical deployment of SLMs.

Main Methods:

  • Literature review of agentic AI in healthcare.
  • Synthesis of evidence on SLM performance, safety, and economics.
  • Analysis of clinical deployment factors, including regulation and governance.

Main Results:

  • SLMs are effective in various agentic healthcare tasks such as clinical documentation, decision support, patient triage, and administrative automation.
  • SLMs demonstrate sufficient capability for most healthcare tasks.
  • SLMs offer significant advantages in deployability, cost, and operational efficiency compared to LLMs.

Conclusions:

  • Small language models are a viable and often preferable alternative to large language models for many agentic healthcare applications.
  • SLMs provide practical benefits for real-world clinical environments.
  • Further research and careful consideration of regulatory aspects are needed for widespread SLM adoption in healthcare.