{
  "abstract": "The first hour after injury, often termed the ‘golden hour,’ relies on rapid and accurate decision making to ensure timely delivery of trauma care. Coordination of prehospital resources, triage, resuscitation, and imaging is critical to patient outcomes. Unlike static algorithms or prediction scores, artificial intelligence (AI) models can integrate large volumes of data to provide actionable decision support. This narrative review explores current and emerging applications of AI in trauma care, specifically focusing on its role within the trauma bay.",
  "authors": [
    {
      "affiliations": [
        "Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA"
      ],
      "name": "Emma Gilman Burke"
    },
    {
      "affiliations": [
        "Department of Surgery, Stanford University, Palo Alto, California, USA"
      ],
      "name": "Chloe Nobuhara"
    },
    {
      "affiliations": [
        "Department of Surgery, Stanford University, Palo Alto, California, USA"
      ],
      "name": "Alex H Lee"
    },
    {
      "affiliations": [
        "Department of Surgery, Stanford University, Palo Alto, California, USA"
      ],
      "name": "Joshua Aaron Villarreal"
    },
    {
      "affiliations": [
        "Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA"
      ],
      "name": "Caitlin Anne Fitzgerald"
    },
    {
      "affiliations": [
        "Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas, USA"
      ],
      "name": "Ryan Peter Dumas"
    }
  ],
  "title": "Ghost in the Machine: Leveraging artificial intelligence in the trauma bay",
  "uid": "2a9b0196-2070-5c97-af74-83ec63eb1530"
}