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Updated: Dec 12, 2025

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A Perspective on Using Machine Learning in 3D Bioprinting.

Chunling Yu1, Jingchao Jiang2

  • 1Faculty of Maritime and Transportation, Ningbo University, Ningbo 315211, China.

International Journal of Bioprinting
|August 13, 2020
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) can enhance three-dimensional (3D) bioprinting by optimizing processes and improving accuracy. This review explores ML applications in 3D printing and their potential to advance 3D bioprinting technologies.

Keywords:
3D printingBioprintingMachine learning

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Area of Science:

  • Biomedical Engineering
  • Materials Science
  • Computer Science

Background:

  • Three-dimensional (3D) printing is increasingly utilized across industries, with 3D bioprinting emerging for biomedical applications.
  • Machine learning (ML) has demonstrated success in optimizing conventional 3D printing processes, including defect detection and material property prediction.

Purpose of the Study:

  • To review existing ML applications in traditional 3D printing.
  • To discuss the potential benefits and applications of ML in 3D bioprinting.

Main Methods:

  • Literature review of machine learning techniques applied to 3D printing.
  • Discussion of ML's potential impact on 3D bioprinting processes.

Main Results:

  • ML is actively used in 3D printing for process optimization, accuracy analysis, defect detection, and material prediction.
  • Limited research currently exists on ML applications specifically within 3D bioprinting.

Conclusions:

  • Machine learning holds significant potential to advance the field of 3D bioprinting.
  • Further research integrating ML into 3D bioprinting is encouraged to drive innovation.