Evidence map›Paper›PMID 41441137›Full record

ReviewGels (Basel, Switzerland)2025

Artificial Intelligence Informed Hydrogel Biomaterials in Additive Manufacturing.

Zhizhou Zhang, Zach Z Tao, Ruiling Du, Runxin Huo, Xiangrui Zheng

Abstract readReview
In one paragraph

Review in Gels (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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.

Zhizhou ZhangSchool of Engineering, The University of Manchester, Manchester M13 9PL, UK.
Zach Z TaoSchool of Engineering, The University of Manchester, Manchester M13 9PL, UK.
Ruiling DuSchool of Materials Science and Engineering, Central South University, Changsha 410083, China.
Runxin HuoSchool of Engineering, The University of Manchester, Manchester M13 9PL, UK.ORCID 0000-0002-1142-3784
Xiangrui ZhengDepartment of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore.

Funding

UK Research and Innovation EP/R00661X/1 and EP/X527257/1
6 · The paper itself

Abstract

Hydrogel additive manufacturing underpins soft tissue models, biointerfaces, and soft robotics. The coupled choices of formulation, rheology, and process conditions limit the progress. This review maps how artificial intelligence links composition to printability across direct ink writing, inkjet, vat photopolymerization, and laser-induced forward transfer, and how vision-guided control improves fidelity and viability during printing. Interpretable predictors connect routine rheology to strand stability, data-driven classifiers chart droplet regimes, and optical dose models with learning enhance voxel accuracy. Polymer informatics, including BigSMILES based representations, supports generative screening of precursors and crosslinkers. Bayesian optimization and active learning reduce experimental burden while honoring biological constraints, and emerging autonomous platforms integrate in situ sensing with rapid iteration. A strategic framework outlines a technological progression from current open-loop data gathering toward real-time closed-loop correction and ultimately predictive fault prevention through digital twins. The synthesis provides quantitative routes from formulation through process to function, establishing a practical foundation for predictive, reproducible hydrogel manufacturing and application-oriented design.

Indexed as

additive manufacturingbiomaterialshydrogelmachine learningmaterial design

Identifiers

PMID41441137
PMCPMC12733118

What OpenQuestion 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 OpenQuestion graph.