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Purpose of Review <p>This review addresses the convergence of 3D bioprinting with artificial intelligence (AI) and machine learning (ML) as a new frontier in healthcare to revolutionize personalized medicine. The article first discusses several key components such as pre-printing, material selection, post-printing, and role of AI/ML algorithms during the printing process by predicting tissue construct outcomes and optimizing bioprinting parameters. It follows with discussion of applications of AI-integrated 3D bioprinting such as for drug testing models, surgical planning, and the production of regenerative combination therapies. Finally, it discussed several challenges including ethics, data privacy and security, and governmental regulations in the field of AI driven bioprinting process.</p> Recent Findings <p>By leveraging AI within the 3D bioprinting process, healthcare practitioners can enhance bioprinting fidelity and automate error corrections. Overall, there are several key components of AI-driven bioprinting, including aspects of pre-printing, material selection, and post-printing. AI/ML algorithms can play a vital role during the printing process by predicting tissue construct outcomes and optimizing bioprinting parameters. Additionally, cybersecurity measures, including blockchain and secure networks, are essential to safeguard the bioprinting process against potential threats. Applications of AI-integrated 3D bioprinting include drug testing models, surgical planning, and the production of regenerative combination therapies. Despite the potential of integrating AI and 3D bioprinting in healthcare, several challenges have been identified, including ethics, data privacy and security, and governmental regulations.</p> Summary <p> This review highlights the promise of AI in healthcare, which is vast, and aims to understand its potential for the 3D bioprinting area. AI requires robust governance frameworks, ethical standards, and interdisciplinary collaboration to ensure responsible innovation, transparency, and regulatory alignment. By addressing these considerations, scientistswill be critical to accelerate AI driven bioprinting and deliver impactful, patient-centered solutions in the future.</p>

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Integrating 3D Bioprinting and AI in Healthcare

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Eva Mueller,
  • Salil Desai,
  • Martha Shauck Lundberg,
  • Sita Somara,
  • Carlos Kengla,
  • James Schmidt,
  • Kunal Mitra

摘要

Purpose of Review

This review addresses the convergence of 3D bioprinting with artificial intelligence (AI) and machine learning (ML) as a new frontier in healthcare to revolutionize personalized medicine. The article first discusses several key components such as pre-printing, material selection, post-printing, and role of AI/ML algorithms during the printing process by predicting tissue construct outcomes and optimizing bioprinting parameters. It follows with discussion of applications of AI-integrated 3D bioprinting such as for drug testing models, surgical planning, and the production of regenerative combination therapies. Finally, it discussed several challenges including ethics, data privacy and security, and governmental regulations in the field of AI driven bioprinting process.

Recent Findings

By leveraging AI within the 3D bioprinting process, healthcare practitioners can enhance bioprinting fidelity and automate error corrections. Overall, there are several key components of AI-driven bioprinting, including aspects of pre-printing, material selection, and post-printing. AI/ML algorithms can play a vital role during the printing process by predicting tissue construct outcomes and optimizing bioprinting parameters. Additionally, cybersecurity measures, including blockchain and secure networks, are essential to safeguard the bioprinting process against potential threats. Applications of AI-integrated 3D bioprinting include drug testing models, surgical planning, and the production of regenerative combination therapies. Despite the potential of integrating AI and 3D bioprinting in healthcare, several challenges have been identified, including ethics, data privacy and security, and governmental regulations.

Summary

This review highlights the promise of AI in healthcare, which is vast, and aims to understand its potential for the 3D bioprinting area. AI requires robust governance frameworks, ethical standards, and interdisciplinary collaboration to ensure responsible innovation, transparency, and regulatory alignment. By addressing these considerations, scientistswill be critical to accelerate AI driven bioprinting and deliver impactful, patient-centered solutions in the future.