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21 April 2020 Achieving a trusted, reliable, AI-ready infrastructure for military medicine and civilian care
Cindy Crump, Loretta M. Schlachta-Fairchild
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Proceedings Volume 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II; 114130C (2020) https://doi.org/10.1117/12.2557514
Event: SPIE Defense + Commercial Sensing, 2020, Online Only
Abstract
It is envisioned that significant improvements in medical capabilities may be required to meet formidable conditions expected in future military conflicts and global events such as the COVID-19 pandemic. Similar challenges may exist for large-scale humanitarian assistance missions and civilian mass casualty events that do not conform to prior assumptions for care delivery including evacuation within the golden hour and availability of large medical footprints in non-traditional and field settings. The importance of standardization and foundational infrastructure for medical devices, sensors, and data management is presented in order to achieve safe, and effective medical systems that deliver dramatic advances in functionality made possible by Artificial Intelligence and Machine Learning (AI/ML). The concept of autonomous, artificial intelligence based learning systems for medical support in military Multi-Domain Operations (MDO) to meet evolving demands is presented. Drivers towards greater use of Artificial Intelligence (AI) and Medical Autonomy to solve anticipated gaps in forward resuscitative and stabilization care, as well as associated relevance and implications for the management of civilian disasters are introduced. Finally, the central role of application architecture and robust technology frameworks necessary to advance the state of the science are discussed.
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Cindy Crump and Loretta M. Schlachta-Fairchild "Achieving a trusted, reliable, AI-ready infrastructure for military medicine and civilian care", Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 114130C (21 April 2020); https://doi.org/10.1117/12.2557514
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Cited by 2 scholarly publications.
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KEYWORDS
Artificial intelligence
Medical research
Sensors
Medical devices
Surgery
Systems modeling
Machine learning
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