战场
军事医学
医疗急救
毒物控制
海军
自杀预防
职业安全与健康
伤害预防
人为因素与人体工程学
医学
航空学
计算机安全
工程类
计算机科学
政治学
历史
病理
古代史
法学
作者
Jeremy Pamplin,Mason H Remondelli,Nathan Fisher,Matthew T. Quinn
标识
DOI:10.1093/milmed/usae377
摘要
Artificial intelligence, machine learning, and automation have become ubiquitous in the twenty-first century.Transportation services, automobile manufacturers, the technology industry, and distribution companies all use human-technology teaming (HTTs) to enhance the efficiency of their networks and products.2][3][4] Just as the integration of automation will enable combat units to be more successful, efficient, and deadlier in their mission, it is evident that automation should be applied to disease nonbattle injury and combat casualty care to optimize the efficiency and, therefore, the capability and capacity, of the Military Health System (MHS) to ensure Warfighter survivability.The MHS must adopt an automation paradigm to achieve its goals-minimizing casualties by optimizing health, maximizing casualty return to duty, optimizing battlefield casualty clearance while maintaining or exceeding current casualty outcomes, and overcoming contested logistics-in the complex context of multi-domain battle against peer adversaries.
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