ABSTRACT Recent advancements in stem cell research forge them into one of the most promising sources for cell therapy applications. Quality monitoring in stem cell culture is essential for ensuring consistency, viability, and therapeutic efficacy. Traditional methods involve periodic sampling for conducting endpoint assays such as cell viability, proliferation, and differentiation using microscopy and flow cytometry, which are labor‐intensive and often lack the real‐time monitoring of the processes for scale‐up applications. This paper explores artificial intelligence (AI)‐driven approaches for real‐time quality control, integrating machine vision, predictive modeling, and sensor‐based monitoring. AI models analyze high‐resolution imaging and multi‐sensor data to dynamically track critical quality attributes (CQAs), including cell morphology, proliferation rate, differentiation potential, environmental stability (pH, oxygen, and nutrient levels), genetic integrity, and contamination risks. These models enable automated anomaly detection, differentiation tracking, and adaptive culture optimization. By leveraging real‐time feedback systems and multi‐omics integration, AI‐driven techniques enhance scalability, reproducibility, and process automation in stem cell biomanufacturing. This review outlines current advancements, challenges, and future directions in AI‐assisted quality monitoring and highlights its potential to improve fully automated, scalable production of stem cell lines for clinical translation and regulatory compliance in regenerative medicine.
Singh et al. (2025) studied this question.