The application of artificial intelligence in cryopreservation: Technological advances and future challenges

Abstract

This review systematically sorts out the latest research progress and application status of artificial intelligence (AI) technology in the field of cryopreservation, with a focus on its action mechanisms in aspects such as protocol optimization, damage prediction, and quality control in the cryopreservation of biological samples like cells, embryos, and tissues. From the perspectives of cross-scale modeling, dynamic process optimization, and prediction of structural and functional states, the article analyzes the advantages and challenges of AI in improving the efficiency of cryopreservation, reducing cell damage, and increasing the survival rate after resuscitation. It also summarizes the key application cases in the design of cryoprotectant (CPA) formulations, the control of the freezing or thawing process, and the monitoring of long-term stability. In view of the deficiencies of current AI models in sample diversity, mechanism analysis, and clinical translation, the review puts forward future development directions, including multi-modal data fusion, enhanced interpretability, and the construction of intelligent adaptive control systems. This research provides a reference for the intelligent development of cryopreservation technology and lays a theoretical foundation for subsequent clinical and industrial applications.