Perspectives for artificial intelligence in bioprocess automation
Recent advances in artificial intelligence (AI) have rapidly changed the lab automation landscape, promoting self-driving laboratories (SDLs) that enable autonomous scientific discovery. These trends are increasingly applied in bioprocess development, yet bioprocessing faces unique challenges - biological complexity, regulatory and safety requirements, and multiscale experimentation - that distinguish it from other automation domains. Rather than pursuing full autonomy, we foresee that hybrid SDLs, combining AI-driven decision-making with sustained human oversight, represent the most practical near-term trajectory. This review examines three interconnected perspectives: (i) hybrid human-machine decision-making for bioprocessing; (ii) laboratory design considerations in the era of AI; and (iii) scale-up challenges when transitioning from screening to manufacturing. We highlight critical gaps in data standardization and the required community efforts necessary to realize autonomous bioprocess innovation.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- US Department of Energy; USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23), Biological Systems Science Division (SC-23.2 )
- Grant/Contract Number:
- AC02-05CH11231
- OSTI ID:
- 3012659
- Journal Information:
- Current Opinion in Biotechnology, Journal Name: Current Opinion in Biotechnology Vol. 97
- Country of Publication:
- United States
- Language:
- English
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