Evidence map›Paper›PMID 38464497›Full record

ReviewMaterials today. Bio2024

AI energized hydrogel design, optimization and application in biomedicine.

Zuhao Li, Peiran Song, Guangfeng Li, Yafei Han, Xiaoxiang Ren, Long Bai, Jiacan Su

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Materials today. Bio, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers.

0numbers the graph read from it
0cells of the map it votes in
80citing 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

80 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Artificial intelligence virtual bone organoids (AIVBOs).Journal of orthopaedic translation · 2026
    Review
  6. Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Review
  12. Review
  13. Review
  14. Review
  15. Microengineered Gradient Hydrogels for Mechanobiology.Advanced healthcare materials · 2026
    Review
  16. Article
  17. Review
  18. Article
  19. Review
  20. Review

20 more citing papers are in PubMed but not listed here.

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

7 authors.

Zuhao LiDepartment of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
Peiran SongOrganoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China.
Guangfeng LiOrganoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China.
Yafei HanOrganoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China.
Xiaoxiang RenOrganoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China.
Long BaiOrganoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China.
Jiacan SuDepartment of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional hydrogel design and optimization methods usually rely on repeated experiments, which is time-consuming and expensive, resulting in a slow-moving of advanced hydrogel development. With the rapid development of artificial intelligence (AI) technology and increasing material data, AI-energized design and optimization of hydrogels for biomedical applications has emerged as a revolutionary breakthrough in materials science. This review begins by outlining the history of AI and the potential advantages of using AI in the design and optimization of hydrogels, such as prediction and optimization of properties, multi-attribute optimization, high-throughput screening, automated material discovery, optimizing experimental design, and

Indexed as

Artificial intelligenceBiomedicine applicationDesignHydrogelOptimization

Identifiers

PMID38464497
PMCPMC10924066

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.