Evidence map›Paper›PMID 39194448›Full record

ReviewBiomimetics (Basel, Switzerland)2024

Machine Learning Assists in the Design and Application of Microneedles.

Wenqing He, Suixiu Kong, Rumin Lin, Yuanting Xie, Shanshan Zheng, Ziyu Yin, Xin Huang, Lei Su, Xueji Zhang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Biomimetics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Article
  6. Smart micro/nanoneedles for gene delivery.Iranian journal of basic medical sciences · 2026
    Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Review
  12. 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

9 authors.

Wenqing HeGuangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518000, China.
Suixiu KongGuangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518000, China.
Rumin LinGuangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518000, China.
Yuanting XieSchool of Biomedical Engineering, Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China.
Shanshan ZhengSchool of Biomedical Engineering, Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China.
Ziyu YinSchool of Biomedical Engineering, Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China.
Xin HuangInstitute of Chemical Materials, China Academy of Engineering Physics, Mianyang 621900, China.
Lei SuSchool of Biomedical Engineering, Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China.
Xueji ZhangGuangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen 518000, China.

Funding

Medical-Engineering Interdisciplinary Research Foundation of Shenzhen University 2023YG004Shenzhen Science and Technology Innovation Commission JCYJ20220531101202005Shenzhen Science and Technology Program KQTD20221101093605019the National Key Research and Development Project 2023YFC3605300, 2023YFC3605303
6 · The paper itself

Abstract

Microneedles (MNs), characterized by their micron-sized sharp tips, can painlessly penetrate the skin and have shown significant potential in disease treatment and biosensing. With the development of artificial intelligence (AI), the design and application of MNs have experienced substantial innovation aided by machine learning (ML). This review begins with a brief introduction to the concept of ML and its current stage of development. Subsequently, the design principles and fabrication methods of MNs are explored, demonstrating the critical role of ML in optimizing their design and preparation. Integration between ML and the applications of MNs in therapy and sensing were further discussed. Finally, we outline the challenges and prospects of machine learning-assisted MN technology, aiming to advance its practical application and development in the field of smart diagnosis and treatment.

Indexed as

applicationdesign principledisease treatmentmachine learningmicroneedlesensing

Identifiers

PMID39194448
PMCPMC11351824

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.