美国宇航局深空网络
太空探索
计算机科学
空格(标点符号)
系统工程
航天器
生物监测
人工智能
数据科学
风险分析(工程)
工程类
医学
航空航天工程
生态学
生物
操作系统
作者
Ryan T. Scott,Lauren Sanders,Erik Antonsen,Jaden J. A. Hastings,Seung Min Park,Graham Mackintosh,Robert J. Reynolds,Adrienne Hoarfrost,Aenor Sawyer,Casey S. Greene,Benjamin S. Glicksberg,Corey A. Theriot,Daniel C. Berrios,J. Miller,Joël Babdor,Richard Barker,Sergio E. Baranzini,Afshin Beheshti,Stuart Chalk,Guillermo M. Delgado-Aparicio
标识
DOI:10.1038/s42256-023-00617-5
摘要
Human exploration of deep space will involve missions of substantial distance and duration. To effectively mitigate health hazards, paradigm shifts in astronaut health systems are necessary to enable Earth-independent healthcare, rather than Earth-reliant. Here we present a summary of decadal recommendations from a workshop organized by NASA on artificial intelligence, machine learning and modelling applications that offer key solutions toward these space health challenges. The workshop recommended various biomonitoring approaches, biomarker science, spacecraft/habitat hardware, intelligent software and streamlined data management tools in need of development and integration to enable humanity to thrive in deep space. Participants recommended that these components culminate in a maximally automated, autonomous and intelligent Precision Space Health system, to monitor, aggregate and assess biomedical statuses. Deep-space exploration missions require new technologies that can support astronaut health systems as well as biological monitoring and research systems that can function independently from Earth-based mission control centres. A NASA workshop explored how artificial intelligence advances could help address these challenges and, in this first of two Review articles based on the findings from the workshop, a vision for autonomous biomonitoring and precision space health is discussed.
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