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AI-Enhanced POCUS in Emergency Care.

Monica Puticiu1, Diana Cimpoesu2,3, Florica Pop1

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Summary

Artificial intelligence (AI) enhances point-of-care ultrasound (POCUS) in emergency medicine for faster diagnoses. While promising for trauma and non-traumatic emergencies, AI-POCUS needs more validation for widespread clinical use.

Keywords:
POCUSartificial intelligencedeep learningdiagnostic imagingemergency medicinemachine learningpoint-of-care ultrasound

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Area of Science:

  • Emergency Medicine
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Point-of-care ultrasound (POCUS) is vital in emergency medicine for rapid bedside assessments.
  • Current POCUS use faces limitations like operator dependency and variable image quality.
  • Artificial intelligence (AI) offers potential to improve POCUS acquisition, interpretation, and analysis.

Purpose of the Study:

  • To review current evidence on AI-enhanced POCUS applications in emergency care.
  • To explore AI's role in trauma, non-traumatic emergencies, workflows, resource-limited settings, and education.
  • To identify limitations and future directions for AI-POCUS integration.

Main Methods:

  • This is a narrative review synthesizing existing research on AI-enhanced POCUS.
  • Evidence was gathered on applications across various emergency scenarios.
  • The review focuses on AI's impact on image acquisition, interpretation, and clinical decision support.

Main Results:

  • AI-assisted POCUS shows promise in trauma for automated detection of conditions like pneumothorax and hemothorax.
  • AI-enabled applications in non-traumatic emergencies offer automated measurements and pattern recognition.
  • Integrated AI-POCUS systems can expand access and support non-expert users, but limitations like generalizability and dataset bias persist.

Conclusions:

  • AI-enhanced POCUS is moving towards clinical integration in emergency medicine as a decision-support tool.
  • Current evidence suggests AI-POCUS can improve consistency and efficiency.
  • Widespread adoption requires further validation, representative datasets, and regulatory alignment.