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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
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Whole-body PET/MRI of Pediatric Patients: The Details That Matter
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Triage and workflow optimization with artificial intelligence in pediatric imaging.

Harsimran Bhatia1, Anmol Bhatia1, Arhanjit Singh2

  • 1Post Graduate Institute of Medical Education and Research, Chandigarh, India.

Pediatric Radiology
|December 8, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and machine learning (ML) optimize pediatric radiology workflows. Integrating multimodal data can enhance AI triage models for improved efficiency and patient outcomes.

Keywords:
Artificial intelligencePediatricTriageWorkflow

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Healthcare systems face increasing burdens, necessitating AI for patient triage and workflow optimization.
  • Machine learning (ML) algorithms are central to AI software, assisting healthcare professionals in patient care.
  • AI is increasingly used in radiology, aiding image acquisition, referrals, scheduling, and radiation dose management.

Purpose of the Study:

  • To review the utility of AI and ML algorithms in pediatric radiology.
  • To highlight how these technologies aid in patient triage and workflow streamlining.
  • To discuss the potential of multimodal datasets for future AI development in pediatric radiology.

Main Methods:

  • Review of current AI and ML applications in pediatric radiology.
  • Analysis of AI's role in image acquisition, patient referrals, and scheduling.
  • Discussion on the impact of technical challenges and data limitations.

Main Results:

  • AI algorithms have significantly improved image acquisition and workflow efficiency in pediatric radiology.
  • ML-based software assists in accurate referrals, optimized scheduling, radiation dose management, and follow-up reminders.
  • Current limitations include technical challenges and a scarcity of pediatric datasets.

Conclusions:

  • AI and ML are revolutionizing pediatric radiology by enhancing triage and streamlining workflows.
  • Multimodal pediatric datasets are crucial for developing adaptable AI triage models.
  • Future integration of AI promises enhanced efficiency, reduced turnaround times, and improved patient outcomes.