Evidence map›Paper›PMID 39651398›Full record

ReviewBioactive materials2025

AI-driven 3D bioprinting for regenerative medicine: From bench to bedside.

Zhenrui Zhang, Xianhao Zhou, Yongcong Fang, Zhuo Xiong, Ting Zhang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Bioactive materials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

53 citing papers in PubMed.

  1. Review
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  4. Natural Polymers in Guided Bone Regeneration (GBR).Journal of functional biomaterials · 2026
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  6. Review
  7. Review
  8. Thiolated Polymers in 3D Bioprinting: Control of Gelation.Advanced materials (Deerfield Beach, Fla.) · 2026
    Review
  9. Review
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  11. Review
  12. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zhenrui ZhangBiomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China.
Xianhao ZhouBiomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China.
Yongcong FangBiomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China.
Zhuo XiongBiomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China.
Ting ZhangBiomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent decades, 3D bioprinting has garnered significant research attention due to its ability to manipulate biomaterials and cells to create complex structures precisely. However, due to technological and cost constraints, the clinical translation of 3D bioprinted products (BPPs) from bench to bedside has been hindered by challenges in terms of personalization of design and scaling up of production. Recently, the emerging applications of artificial intelligence (AI) technologies have significantly improved the performance of 3D bioprinting. However, the existing literature remains deficient in a methodological exploration of AI technologies' potential to overcome these challenges in advancing 3D bioprinting toward clinical application. This paper aims to present a systematic methodology for AI-driven 3D bioprinting, structured within the theoretical framework of Quality by Design (QbD). This paper commences by introducing the QbD theory into 3D bioprinting, followed by summarizing the technology roadmap of AI integration in 3D bioprinting, including multi-scale and multi-modal sensing, data-driven design, and in-line process control. This paper further describes specific AI applications in 3D bioprinting's key elements, including bioink formulation, model structure, printing process, and function regulation. Finally, the paper discusses current prospects and challenges associated with AI technologies to further advance the clinical translation of 3D bioprinting.

Indexed as

3D bioprintingArtificial intelligenceClinical translationMachine learningQuality by designRegenerative medicine

Identifiers

PMID39651398
PMCPMC11625302

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.