Artificial intelligence (AI) and machine learning are transforming how complex biological systems are studied, enabling researchers to extract meaningful insight from large and multidimensional datasets generated across modern cellular and biomaterials research. Within CeMi, the AI pillar supports the analysis and integration of diverse data sources, including high-content imaging, mechanical measurements, and molecular datasets, to better understand how cells interact with engineered microenvironments. By combining computational tools with experimental approaches,

AI methods allow researchers to identify subtle patterns in cell behaviour, quantify responses to materials and stimuli, and accelerate the discovery of mechanisms that drive tissue function and regeneration.

The AI pillar aims to strengthen CeMi’s ability to translate advanced data analysis into practical research workflows across its core themes, including stem cell engineering, biomaterials, and in vitro tissue models. By promoting collaboration across disciplines, developing shared computational tools, and supporting training in modern data science techniques, this pillar helps ensure that AI-enabled methods become an integral part of experimental design and discovery. Ultimately, the goal is to establish AI-driven analysis and modelling as a distinctive capability within CeMi, enhancing the centre’s ability to deliver innovative, data-rich insights into the cellular microenvironment and its role in health and disease.