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Area of Science:

  • Medical Technology
  • Data Science in Healthcare
  • Trauma Surgery

Background:

  • Trauma care systems are data-rich and time-sensitive, making them ideal for artificial intelligence (AI) integration.
  • Current trauma care faces challenges in resource allocation and provider expertise variability, particularly in non-traditional settings.

Purpose of the Study:

  • To review current and emerging AI applications across the full spectrum of trauma care.
  • To examine AI's potential in diverse environments, including prehospital, rural, and military settings.
  • To highlight AI's role in enhancing decision-making and patient outcomes in trauma.

Main Methods:

  • Narrative review of existing literature on AI in trauma care.
  • Analysis of AI applications in triage, resuscitation, operative decision-making, intensive care, and complication detection.
  • Exploration of AI's utility in resource-constrained and remote care environments.

Main Results:

  • AI models demonstrate superior performance over conventional methods in predicting injury severity and identifying patients needing intervention.
  • Specific AI tools include triage support, resuscitation sequencing, real-time imaging interpretation, and outcome prediction.
  • AI applications show potential in prehospital, rural, and military trauma care settings.

Conclusions:

  • AI has significant potential to transform trauma care by improving decision-making and patient outcomes.
  • Widespread adoption requires further validation, transparency, and adherence to ethical and regulatory standards.
  • AI implementation can help address disparities in access to high-quality trauma care.