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Predictive modeling in neurocritical care using causal artificial intelligence.

Johnny Dang1, Amos Lal2, Laure Flurin3

  • 1Mayo Clinic Alix School of Medicine, Mayo Clinic, Rochester, MN 55905, United States.

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|July 28, 2021
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Artificial intelligence and digital twins are advancing medicine, particularly in neurocritical care. This review explores opportunities for AI to enhance clinical decision-making and education for future clinicians.

Keywords:
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Area of Science:

  • Computational Neuroscience
  • Medical Informatics
  • Critical Care Medicine

Background:

  • Artificial intelligence (AI) and digital twin models are established in industry for efficient product testing.
  • Digital twins in medicine gained traction with the Archimedes diabetes model (2003) and have since been applied to cardiology, endocrinology, and medical education.
  • Current AI applications in neurocritical care primarily involve electroencephalography interpretation, intracranial pressure monitoring, and outcome prognostication.

Purpose of the Study:

  • To review the current state of AI in neurocritical care.
  • To identify challenges and opportunities in developing actionable AI models for neurocritical care.
  • To explore the potential of AI in educating new clinicians and augmenting clinical decision-making in neurocritical care.

Main Methods:

  • Mini-review of existing literature on AI applications in neurocritical care.
  • Analysis of current AI capabilities, including electroencephalogram interpretation, seizure detection, and brain activity identification.
  • Discussion of the potential for AI to address unmet needs in neurocritical care education and decision support.

Main Results:

  • AI has shown promise in interpreting electroencephalograms, detecting seizures, and identifying brain activation.
  • The application of digital twins and AI in critical care medicine, especially neurocritical care, is less explored compared to chronic disease management.
  • Significant opportunities exist for developing AI models to support clinical decision-making and training in neurocritical care.

Conclusions:

  • AI holds substantial potential to transform neurocritical care by enhancing diagnostic capabilities and clinical decision support.
  • Further development of actionable AI models is crucial for educating the next generation of neurocritical care clinicians.
  • Bridging the gap between current AI capabilities and the specific needs of neurocritical care presents both challenges and significant opportunities.