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International Journal of Bioprinting Bioprinting organoids for toxicity testing

the influence of genetic variations on tumor formation the accuracy and reliability of automation control, and to
and treatment response. Apart from that, microfluidic achieve more accurate cell positioning and environmental
103
technology should be considered in future attempts to simulation. 110-112
simulate physiological processes, such as nutrient delivery
and drug diffusion in the tumor microenvironment, which 6. Conclusions
are instrumental for further improving the biological 3D bioprinting technology provides a unique platform,
authenticity of the cancer model. which can be integrated with innovative techniques

In summary, the future of 3D bioprinting pancreatic available in different fields, for facilitating the progress
cancer models is exciting. With the continuous development in the research and treatment of pancreatic diseases.
and breakthrough of technology, we have reason to With bioprinting technology, functional pancreatic islets
believe that this field will bring more breakthroughs and in vitro tumor models that can accurately simulate
and possibilities for the research and treatment of the environment in vitro can be constructed. Besides,
pancreatic cancer. the integration of AI-based automation control into 3D
bioprinting process can further improve the accuracy
5. Challenges and suggestions and controllability of the model, allowing the guidance of
personalized medicine and promoting drug research and
When integrating AI-based automation to 3D bioprinting development.
in the fabrication of functional pancreatic islets and in vitro
tumor models, technical complexity remains the foremost Acknowledgments
challenge. Multiple factors, such as material selection, cell
orientation, scaffold design, and AI-based automation control The authors gratefully thank Professor Yongchan Huang,
involving algorithm optimization and training, should be Assistant Professor, University of Hong Kong, and Jixian
under consideration while using the bioprinting technologies, Liu, the Director of Thoracic Surgery, Peking University
which are expected to solve complex problems in an inter- Shenzhen Hospital, for their helpful suggestions. The
disciplinary manner. 104-107 Another challenge is the accuracy authors would like to express their gratitude to AJE for the
and reliability of the models. Although 3D bioprinting expert linguistic services provided.
technology can build functional models of islets and tumors,
whether the models can fully simulate the physiological Funding
environment in vivo still needs to be further verified. This work was supported by Scientific Research

The accuracy of AI-based automation control in Project of Education Department of Anhui Province
model construction and printing process also needs to be (YJS20210324), the National Natural Science Foundation
continuously improved to ensure the accuracy of the printed of China (81972829), the Scientific Research Foundation
cell localization and environmental simulation. In addition, of Peking University Shenzhen Hospital (KYQD202100X),
data acquisition and processing is also a key aspect. A large Research and Development of Intelligent Surgical
volume of data, including cell characteristics and drug Navigation and Operating System for Precise Liver
response, are required for optimizing the AI technology, Resection (2022ZLA006), Start-up Fund for Talent
but the acquisition and processing of this sort of data may Researchers of Tsinghua University (10001020507), and
be restrained by technical and privacy challenges. At National Science and Technology Major Project of China
108
the same time, professional data analysis and processing (2017ZX100203205).
means are also required for accurate extraction of useful
information from a broad swathe of data to support the Conflict of interest
optimization and application of AI models. In response to The authors declare no competing conflicts of interest.
109
these challenges, we make the following recommendations:
First, multidisciplinary cooperation, combining expertise Author contributions
in biology, medicine, engineering and other fields, should
be strengthened to jointly solve the problems in technical Conceptualization: Liusheng Wu, Zhengyang Fan, Jingyi
complexity and model accuracy. Second, data sharing Xu, Ning Li, Xinye Qian, Wang Hu, Shuang Wang
should be promoted and databases should be established Supervision: Jun Yan
to support the training and validation of AI models, while Writing – original draft: Liusheng Wu
ensuring data privacy and security. In addition, the AI Writing – review & editing: Zewei Lin, Xiaoqiang Li,
algorithm should be continuously optimized to improve Jun Yan

Volume 10 Issue 1 (2024) 137 https://doi.org/10.36922/ijb.1256