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Related Concept Videos

Current Trends in Nursing II01:30

Current Trends in Nursing II

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Trends in nursing are multifactorial and associated with changes in society, within the nursing profession, and in other professions. Notably, telehealth and remote nursing contribute to successful healthcare delivery for numerous patients and help reduce stress for nurses due to nursing shortages. Nurses can reach patients, monitor their conditions, and interact with them using computers, audio, visual accessories, and telephones—for example, remote patient monitoring systems. Likewise,...
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Ethical Issues01:27

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Nurses are essential in patient care, upholding the ethical principles of their profession and effectively navigating ethical dilemmas. Neglecting ethical issues can lead to inadequate patient care, compromised therapeutic relationships, and moral distress among healthcare workers.
Ethical Concerns in Healthcare:
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Ethical Dilemmas I01:17

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Ethical dilemmas in nursing are of utmost importance, as they often arise from the tension between adhering to core ethical principles and the practical realities of healthcare delivery. These dilemmas require nurses to navigate complex situations where competing ethical considerations pull them in different directions.
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Take the case of caring for minors, particularly in areas related to reproductive...
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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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Standards of Care II01:19

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Standards of Care I01:22

Standards of Care I

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Federal statutes profoundly impact nursing practice, providing critical guidelines to ensure patient care is equitable, accessible, and of the highest quality. The following laws address distinct aspects of healthcare provision and patient rights:
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  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Artificial Intelligence
  9. Natural Language Processing
  11. Artificial Intelligence-driven Clinical Guideline Recommendations In Maternal Care: How Trustworthy Are They?
  1. Home
  3. Research Domains
  5. Information And Computing Sciences
  7. Artificial Intelligence
  9. Natural Language Processing
  11. Artificial Intelligence-driven Clinical Guideline Recommendations In Maternal Care: How Trustworthy Are They?

Related Experiment Video

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
06:11

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

Published on: September 26, 2025

750

Artificial intelligence-driven clinical guideline recommendations in maternal care: How trustworthy are they?

Jairo J Pérez1, Andrés F Giraldo-Forero2, Santiago Rúa3

  • 1Departamento de Ciencias Aplicadas, Instituto Tecnológico Metropolitano, Medellín, Colombia.

Biomedica : Revista Del Instituto Nacional De Salud
|December 18, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Large language models with retrieval-augmented generation can improve clinical guideline adherence for medical staff. GPT-3.5 showed high accuracy and relevance in answering maternal health questions.

Keywords:
Clinical guidelines as topicmaternal healthcare servicesartificial intelligencelarge language models

Related Experiment Videos

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity
06:11

High-definition Transcranial Direct Current Stimulation over Right Dorsolateral Prefrontal Cortex to Enhance Metacognitive Sensitivity

Published on: September 26, 2025

750

Area of Science:

  • Artificial Intelligence in Medicine
  • Clinical Decision Support Systems
  • Health Informatics

Background:

  • Medical staff struggle to apply clinical guidelines effectively.
  • Large language models (LLMs) and retrieval-augmented generation (RAG) offer potential solutions.
  • RAG systems can provide context-specific outputs aligned with medical guidelines.

Purpose of the Study:

  • Evaluate commercial LLMs within RAG systems for maternal health queries.
  • Assess performance using human and automated metrics.
  • Determine the accuracy and reliability of LLM-generated answers based on clinical guidelines.

Main Methods:

  • A controlled experiment using Colombian maternal care guidelines.
  • Physician-formulated questions and groundtruth answers.
natural language processing
  • Testing various LLMs with standardized prompts and evaluating via binary answer-concept ranking and RAG assessment by other LLMs.

    • Main Results:

    • GPT-3.5 achieved the highest physician-assessed accuracy (0.90) and answer relevance (0.94, 0.86).
    • Claude 3.5 and Mistral showed high faithfulness scores under different LLM evaluations (0.78 and 0.84, respectively).
    • Larger models (GPT-3.5, Claude, Llama 70B) outperformed smaller models (Llama 8B).

    Conclusions:

    • RAG integration in obstetrics can enhance evidence-based practice and patient outcomes.
    • Rigorous validation is crucial before clinical deployment of LLM-based systems.
    • LLMs show promise for improving guideline adherence and clinical decision support.