Engineering Intelligent iPSC Biomanufacturing: Integrating Robotic Automation, AI, and Process Control

Scalable manufacturing of human‐induced pluripotent stem cells (iPSCs) is increasingly critical for regenerative medicine, high‐throughput drug discovery and screening, and advanced organ‐on‐chip systems and organoid models. However, conventional manual culture remains labor‐intensive, operator‐dependent, and variable, limiting scalability and reproducibility. Automated iPSC culture platforms address these limitations by integrating robotics, liquid handling, imaging, and controlled incubation to minimize human intervention while improving process consistency and cell quality. Artificial intelligence (AI) and machine‐learning approaches further add an intelligent decision‐making layer to automated workflows. Deep‐learning models enable automated assessment of colony morphology and confluency, detection of spontaneous differentiation, and prediction of growth kinetics and optimal passage timing. AI‐enabled analysis of imaging and sensor data also strengthens real‐time quality assessment and process monitoring, although fully autonomous, closed‐loop control remains an emerging capability. Key challenges include iPSC line‐to‐line variability, differences in growth and differentiation behavior, platform interoperability, and limited standardization of workflows and quality‐control criteria. This review examines commercial and emerging automated iPSC culture technologies, compares manual and automated manufacturing workflows, and evaluates integration with downstream processes. The convergence of robotics, advanced sensing, and AI provides a foundation for scalable, reproducible, and intelligent iPSC biomanufacturing.