Evidence map›Paper›PMID 41476113›Full record

ReviewNature methods2026

AI-empowered super-resolution microscopy: a revolution in nanoscale cellular imaging.

Sen Li, Xiangjie Meng, Bo Zhou, Wenfeng Tian, Liangyi Chen, Yang Zhang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Embedding AI in biology - part 2.Nature methods · 2026
    Article
  2. Review
  3. Review
  4. 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

6 authors.

Sen Li *School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Xiangjie Meng *School of Biomedical Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Bo ZhouNew Cornerstone Science Laboratory, National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China.ORCID http://orcid.org/0000-0002-0719-6179
Wenfeng TianNew Cornerstone Science Laboratory, National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China.
Liangyi ChenNew Cornerstone Science Laboratory, National Biomedical Imaging Center, State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China. lychen@pku.edu.cn.ORCID http://orcid.org/0000-0003-1270-7321
Yang ZhangSchool of Biomedical Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, China. yang.zhang2020@hotmail.com.ORCID http://orcid.org/0000-0002-3503-5161

Funding

National Natural Science Foundation of China (National Science Foundation of China) 81925022National Natural Science Foundation of China (National Science Foundation of China) 82273890
6 · The paper itself

Abstract

Super-resolution microscopy (SRM) has revolutionized nanoscale cellular imaging, providing detailed insights into cellular architecture, organelle organization, molecular interactions and subcellular dynamics. Artificial intelligence (AI) has shown its transformative potential for improving SRM to advance our understanding of complex cellular structures and dynamics. This Review begins by offering a comprehensive overview of AI techniques in computer vision, focusing on their application to SRM. Additionally, this Review provides a thorough summary of publicly available code and datasets that can support the development and evaluation of AI-empowered SRM. Notably, many AI techniques in the domain of computer vision remain underexplored in SRM. The ongoing evolution of AI promises to unlock new potential in SRM, and the integration of cutting-edge AI technologies is poised to pioneer breakthroughs in nanoscale cellular imaging.

Indexed as

Artificial IntelligenceImage Processing, Computer-AssistedMicroscopyNanotechnologyAnimalsHumans

Identifiers

PMID41476113

What OpenQuestion holds

Textmetadata
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.