What is it about?
We review how artificial intelligence (AI) is transforming the field of 3D bioprinting, a technique that prints living cells and biomaterials to create tissues and organs.AI helps overcome these challenges by automating and optimizing key steps, including medical image processing, bioink selection, and precise control of the printing process. By combining classical AI and machine learning, researchers can now build more accurate, personalized, and functional tissues—bringing the reality of engineered organs closer than ever.
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Why is it important?
This paper provides the first comprehensive review covering both classical AI and machine learning approaches in 3D bioprinting. It highlights how AI dramatically improves efficiency, accuracy, and reproducibility across all stages—from design to bioink selection to in situ printing on moving surfaces. Integrating AI addresses long-standing hurdles in bioprinting, such as complexity in optimizing materials and precise, real-time adaptation during printing. This fusion accelerates the development of personalized medical treatments, complex tissue models for drug screening, and even full-scale organs, potentially solving major healthcare challenges such as organ shortages and reliance on animal testing.
Perspectives
Writing this review was particularly rewarding, as it allowed me to connect two rapidly evolving technologies—AI and bioprinting—that I've long believed could greatly benefit each other. Personally, the idea of using intelligent algorithms to overcome the complexity of printing living tissues is fascinating and inspiring. I see enormous potential for AI-driven bioprinting to reshape medicine, and I hope this paper encourages researchers to explore new intersections between technology and biology, ultimately improving healthcare outcomes worldwide.
Dr Hongyi Chen
Read the Original
This page is a summary of: Recent advances and applications of artificial intelligence in 3D bioprinting, Biophysics Reviews, July 2024, American Institute of Physics,
DOI: 10.1063/5.0190208.
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