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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...
Current Trends in Nursing II01:30

Current Trends in Nursing II

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,...
Healthcare Agencies I01:18

Healthcare Agencies I

Healthcare agencies provide healthcare services to people. In the United States, voluntary agencies are often non-profit centers sponsored by donations, grants, or fundraisers. One such organization is Meals on Wheels, which provides meals to the elderly and homebound. The American Heart Association and the American Lung Association are other non-profit community organizations. Doctors and nurses are frequently active members of these organizations, which offer health checks and educational...
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Health Information Technology and Healthcare Information System

Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
Healthcare Agencies II01:17

Healthcare Agencies II

There are various healthcare agencies in the United States—some of which are managed by religious institutions and others by different government branches.
Parish nursing is a growing specialty nursing profession that focuses on holistic healthcare, health promotion, and illness prevention. It blends professional nursing practice with a health ministry, focusing on health and healing within the context of a Christian community. Parish nurses serve as health educators, referral sources, and lay...
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
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Related Experiment Video

Updated: May 28, 2026

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

Explainable Agentic Artificial Intelligence in Healthcare: A Scoping Review.

Bernardo G Collaco1, Srinivasagam Prabha1, Cesar A Gomez-Cabello1

  • 1Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd S, Jacksonville, FL 32224, USA.

Bioengineering (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Explainable agentic AI (XAAI) in healthcare primarily uses intrinsic, workflow-native explanations like reasoning traces. Current evidence is limited, highlighting the need for standardized evaluations and real-world validation for safe integration.

Keywords:
agentic AIartificial intelligenceclinical decision support systemsexplainable AIexplainable agentic AIhealthcare AI

Related Experiment Videos

Last Updated: May 28, 2026

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:

  • Artificial Intelligence in Medicine
  • Healthcare Informatics
  • Explainable AI (XAI)

Background:

  • Agentic AI systems offer autonomous capabilities for healthcare but raise concerns about transparency and oversight.
  • Existing research on explainable AI (XAI) primarily focuses on traditional predictive models, with limited understanding of its application in agentic architectures.

Purpose of the Study:

  • To conduct a scoping review of explainable agentic AI (XAAI) in healthcare.
  • To characterize the types, scope, and forms of explainability employed in these emerging systems.

Main Methods:

  • Scoping review adhering to PRISMA-ScR guidelines.
  • Searched major databases (PubMed, Embase, IEEE Xplore, ACM Digital Library) through November 2025.
  • Extracted data on system architecture, explainability type, scope, form, and clinical outcomes for eligible studies.

Main Results:

  • Nine studies included, all featuring agentic AI with autonomy and tool use, often in multi-agent systems.
  • Explainability was mainly intrinsic and workflow-native, using textual reasoning traces and evidence grounding.
  • XAAI systems showed improved performance and interpretability in radiology, neurology, psychiatry, and research, but studies were heterogeneous and lacked structured human oversight.

Conclusions:

  • Current XAAI emphasizes process transparency and evidence grounding over mechanistic attribution.
  • Limited and heterogeneous evidence suggests early trends, requiring cautious interpretation.
  • Standardized evaluations, clear oversight reporting, and real-world validation are crucial for safe healthcare integration.