Cutting-edge approaches integrate machine learning with UV absorbance spectroscopy to rapidly detect microbial contamination in cell therapies within 30 minutes, enhancing sterility assurance without harming cells. Concurrently, Weill Cornell Medicine employed ChemPerturb-seq, an AI-powered chemical screen, identifying sex-specific small molecule cocktails that improve survival of transplanted pancreatic beta cells, paving the way for therapies in type 1 diabetes. These advances showcase AI’s growing role in accelerating quality control and optimizing cellular therapeutics development within biomanufacturing and regenerative medicine sectors.