Objective:

To review the integration of AI and laboratory automation in bioprocess development and propose a framework for hybrid systems.

Key Findings:

• Current bioprocess automation operates primarily at Levels 1 to 2, requiring significant human oversight.
• A modular hybrid-lab framework can enhance bioprocess development by integrating automated and manual processes.
• Scale-up from small experimental volumes to larger pilot and manufacturing scales presents significant challenges.
• Data standardization and shared protocols are essential for the broader implementation of self-driving laboratories.

Interpretation:

The review suggests that the future of bioprocess labs lies in hybrid systems that leverage both human expertise and AI-driven automation, rather than replacing scientists.

Limitations:

• The review does not provide empirical data to support the proposed frameworks.
• Challenges related to regulatory compliance and safety in hybrid systems are not fully explored.

Conclusion:

The careful design of hybrid systems is crucial for enabling reliable, scalable, and responsible bioprocess innovation.