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The objective of this research was to identify current and future approaches to the design of highly automated systems for life science processes involving humans in control loops in applications such as high-throughput compound screening and high-performance analytical chemistry. (In some advanced applications, screening of biochemical reactions and analytics are performed together.) The identified approaches were classified according to existing theories of human-centered automation, which provided a basis for projecting human performance implications, including error recovery capability. We provide background on the life sciences domain and established theories of types and levels of automation in complex human-machine systems. We describe specific forms of robotic and automated technologies used in life science applications and the general design of high-throughput screening and analytical systems to accommodate particular process configurations. Some example classifications of life science automation (LSA) schemes are presented by referring to a taxonomy of levels of automation from the literature. Finally, we identify the need for future empirical research on human performance consequences of LSA and remedial measures, including enhanced supervisory control interface design.