Artificial intelligence (AI) is becoming increasingly valuable in the field of 3D bioprinting, offering a range of benefits. 3D bioprinting is an emerging technique used to create tissues and even entire organs for research, testing, and implantation. By depositing cells, hydrogels, and biomaterials layer by layer, complex structures can be customized, but this also introduces challenges. In a recent publication, Chen et al. explored the applications of AI in 3D bioprinting and presented various tools and methods to address the complexities of the field and achieve valuable outcomes. AI has the capability to analyze complex data, generate easily interpretable outputs, and perform intricate calculations, which would otherwise be time-consuming and laborious. These characteristics are highly beneficial for solving longstanding issues in 3D bioprinting. According to author Jie Huang, AI is commonly employed in 3D bioprinting for tasks such as medical image reconstruction, where AI algorithms automate the segmentation and modeling of anatomical structures.

Additionally, machine learning can predict optimal bioink formulations for printability and biocompatibility. Real-time monitoring and adjustment of printing parameters is another area where AI can ensure the high fidelity and functionality of printed structures. The authors hope that their overview will inspire more researchers to leverage the potential of AI, unlocking new techniques and applications for bioprinting technology, ultimately benefiting both scientific understanding and clinical practice. Huang further noted, "Promising advancements in automating the entire bioprinting process, including in situ printing, can be achieved through AI. In situ bioprinting, which involves printing tissues and structures directly within the body, has revolutionary potential for repairing and regenerating damaged tissues in real-time during surgical procedures. " Source: "Recent advances and applications of artificial intelligence in 3D bioprinting, " by Hongyi Chen, Bin Zhang, and Jie Huang, Biophysics Reviews (2024). The article is available at https://doi. org/10. 1063/5. 0190208.