Bonding Biology and Data: AI-Driven Advances in Stem Cell Research

Kanchan Karmakar*, Sanbartika Ghosh, Srigata Pandey and Kaushiki Nag

Stem cell–based therapies represent a central component of regenerative medicine owing to their capacities for self-renewal, multilineage differentiation, and disease modelling. However, their clinical translation remains hindered by biological heterogeneity, inconsistent differentiation outcomes, safety concerns, and limitations in scalability. Simultaneously, high-throughput experimental platforms have produced large, complex datasets that challenge conventional analytical methodologies. This review synthesises current literature addressing the application of artificial intelligence (AI), including machine learning and deep learning approaches, in stem cell research and therapy. Emphasis is placed on computational strategies for stem cell identification, characterisation, differentiation analysis, and cell fate prediction, drawing from studies integrating imaging and multi-omics data. Accumulating evidence indicates that AI-driven frameworks substantially enhance the accuracy, reproducibility, and efficiency of stem cell analyses. These approaches enable automated interpretation of high-dimensional datasets, facilitate prediction of lineage commitment, and improve quality assessment of cellular populations. AI methodologies further contribute to experimental optimisation and the development of predictive models supporting regenerative applications. Artificial intelligence is reshaping stem cell research by addressing longstanding analytical and biological challenges. Although technical, regulatory, and ethical limitations persist, continued advancements in AI integration are expected to accelerate the development of robust, scalable, and personalised regenerative therapies.

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Published on: Mar 3, 2026 Pages: 1-9

Full Text PDF Full Text HTML DOI: 10.17352/asb.000028
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