Evidence map›Paper›PMID 37746667›Full record

ReviewMaterials today. Bio2023

Application of artificial intelligence in 3D printing physical organ models.

Liang Ma, Shijie Yu, Xiaodong Xu, Sidney Moses Amadi, Jing Zhang, Zhifei Wang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Materials today. Bio, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

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  19. Perspectives of Additive Manufacturing in 5.0 Industry.Materials (Basel, Switzerland) · 2025
    Review
  20. Review
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

6 authors.

Liang MaCollege of Materials Science and Engineering, Zhejiang University of Technology, Hangzhou, 310000, China.
Shijie YuCollege of Materials Science and Engineering, Zhejiang University of Technology, Hangzhou, 310000, China.
Xiaodong XuCollege of Materials Science and Engineering, Zhejiang University of Technology, Hangzhou, 310000, China.
Sidney Moses AmadiInternational Education College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, 310000, China.
Jing ZhangCollege of Materials Science and Engineering, Zhejiang University of Technology, Hangzhou, 310000, China.
Zhifei WangZhejiang Provincial People's Hospital, Hangzhou, Zhejiang, 310000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) and 3D printing will become technologies that profoundly impact humanity. 3D printing of patient-specific organ models is expected to replace animal carcasses, providing scenarios that simulate the surgical environment for preoperative training and educating patients to propose effective solutions. Due to the complexity of 3D printing manufacturing, it is still used on a small scale in clinical practice, and there are problems such as the low resolution of obtaining MRI/CT images, long consumption time, and insufficient realism. AI has been effectively used in 3D printing as a powerful problem-solving tool. This paper introduces 3D printed organ models, focusing on the idea of AI application in 3D printed manufacturing of organ models. Finally, the potential application of AI to 3D-printed organ models is discussed. Based on the synergy between AI and 3D printing that will benefit organ model manufacturing and facilitate clinical preoperative training in the medical field, the use of AI in 3D-printed organ model making is expected to become a reality.

Indexed as

3D printingArtificial intelligenceOrgan modelsPreoperative training

Identifiers

PMID37746667
PMCPMC10511479

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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 OpenQuestion graph.