Evidence map›Paper›PMID 35750326›Full record

ReviewChemical reviews2022

Artificial Intelligence in Meta-optics.

Mu Ku Chen, Xiaoyuan Liu, Yanni Sun, Din Ping Tsai

Abstract readReview
In one paragraph

Review in Chemical reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.

0numbers the graph read from it
0cells of the map it votes in
47citing 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

47 citing papers in PubMed.

  1. Article
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  8. Designing strongly coupled polaritonic structures via statistical machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Compound meta-optics: there is plenty of room at the top.Nanophotonics (Berlin, Germany) · 2025
    Article
  14. Article
  15. Review
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article
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

4 authors.

Mu Ku ChenDepartment of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong 999077.ORCID 0000-0002-6697-0398
Xiaoyuan LiuDepartment of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong 999077.ORCID 0000-0001-5253-9313
Yanni SunDepartment of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong 999077.
Din Ping TsaiDepartment of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong 999077.ORCID 0000-0002-0883-9906

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent years have witnessed promising artificial intelligence (AI) applications in many disciplines, including optics, engineering, medicine, economics, and education. In particular, the synergy of AI and meta-optics has greatly benefited both fields. Meta-optics are advanced flat optics with novel functions and light-manipulation abilities. The optical properties can be engineered with a unique design to meet various optical demands. This review offers comprehensive coverage of meta-optics and artificial intelligence in synergy. After providing an overview of AI and meta-optics, we categorize and discuss the recent developments integrated by these two topics, namely AI for meta-optics and meta-optics for AI. The former describes how to apply AI to the research of meta-optics for design, simulation, optical information analysis, and application. The latter reports the development of the optical Al system and computation via meta-optics. This review will also provide an in-depth discussion of the challenges of this interdisciplinary field and indicate future directions. We expect that this review will inspire researchers in these fields and benefit the next generation of intelligent optical device design.

Indexed as

Artificial IntelligenceOptics and Photonics

Identifiers

PMID35750326
PMCPMC9562283

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.