This review demonstrates AI's impact on therapeutic potential and material design in regenerative medicine, highlighting predictive models improving wound healing outcomes.
Key Points
Regenerative medicine is progressing with AI-based models, enhancing therapeutic potential and biological properties.
Deep learning models predict stem cell differentiation and immunomodulatory function using imaging data and morphology data.
Machine learning models shift material design from trial-and-error to optimized screening of biomaterials for improved healing.
Addressing challenges in model predictions is crucial for bridging the translational gap to in vivo validation.