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Perspectives for artificial intelligence in bioprocess automation.

Laura Marie Helleckes1, Sebastian Putz2, Kshitiz Gupta3

  • 1The Sargent Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London, United Kingdom; I-X Centre for AI in Science, Imperial College London, London, United Kingdom.

Current Opinion in Biotechnology
|November 29, 2025
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Summary
This summary is machine-generated.

Hybrid self-driving laboratories (SDLs), integrating artificial intelligence (AI) with human oversight, offer a practical path for advancing bioprocess development. This approach addresses unique bioprocessing challenges for efficient scientific discovery.

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Area of Science:

  • Biotechnology
  • Artificial Intelligence
  • Laboratory Automation

Background:

  • Artificial intelligence (AI) is transforming lab automation, leading to self-driving laboratories (SDLs) for autonomous discovery.
  • Bioprocess development presents unique challenges like biological complexity, regulatory demands, and multiscale experiments, differentiating it from other automation fields.

Purpose of the Study:

  • To review the application of AI and SDLs in bioprocess development.
  • To explore hybrid human-machine decision-making models for bioprocessing.
  • To examine laboratory design and scale-up challenges in the context of AI-driven bioprocessing.

Main Methods:

  • Literature review of AI in lab automation and bioprocess development.
  • Analysis of hybrid human-machine decision-making frameworks.
  • Examination of laboratory design principles for AI integration.
  • Discussion of scale-up challenges from bioprocess screening to manufacturing.

Main Results:

  • Hybrid SDLs, combining AI with human oversight, are identified as the most practical near-term solution for bioprocess development.
  • Key challenges include biological complexity, regulatory hurdles, and multiscale experimentation.
  • Gaps in data standardization and community collaboration are critical for realizing autonomous bioprocess innovation.

Conclusions:

  • Hybrid SDLs offer a pragmatic approach to leveraging AI in bioprocessing.
  • Addressing data standardization and fostering community efforts are essential for future advancements.
  • Sustained human oversight remains crucial in AI-driven bioprocess development.