Applications of artificial intelligence in stem cell therapy

Abstract

Stem cell therapy holds great promise for the regeneration and repair of damaged tissues and organs. Stem cell therapy has been successfully applied to treat diseases that cannot be cured with conventional medicine. A careful evaluation of the outcomes is required for successful implementation of stem cell therapy. Recently, artificial intelligence (AI) has opened new avenues for research in the stem cell therapy field. The integration of AI can assist in evaluating the quality, efficiency and safety of stem cells by analyzing available data. It has the potential to improve and accelerate progress in various aspects of stem cell research and therapeutic applications. AI is still in its infancy and has certain limitations, such as algorithm validation problems, inadequate data availability, poor data quality, and ethical considerations. Considering the potential of AI to improve stem cell research and therapeutics, this review aims to explore applications of AI in understanding stem cell behavior, identification and characterization, optimization of the delivery methods, stem cell modeling and prediction of mortality risk. In addition, this review highlights the role of AI, machine learning, deep learning, and other subtypes in advancing stem cell biology research. This review also discusses the current limitations, ethical considerations, and future prospective of use of AI in stem cell research and therapeutic applications.

Keywords: Artificial intelligence; Neural network; Machine learning; Stem cell therapy; Regenerative potential

Core Tip: Stem cell therapy can repair and regenerate tissues and organs, and it has been successfully applied to treat diseases that cannot be cured with traditional medicine. The integration of artificial intelligence (AI) with stem cell therapy has the potential to transform the field of regenerative medicine. AI can analyze data to evaluate quality, efficiency, and safety of stem cells. It can accelerate the progress of stem cell research and medicine. However, the AI field is still new and has challenges, including algorithm validation, data availability, poor data quality, and ethical concerns.