ReviewSmall methods2026
Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.
Review in Small methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed.
- Self-Driving Scanning Probe Microscopy: From Acceleration to Discovery and Manipulation.Accounts of chemical research · 2026Article
- Adaptive Edge-Response-Based Subpixel Localization Method for Microscopic Vision-Based Alignment Measurement.Sensors (Basel, Switzerland) · 2026Article
- Review
- A flexible framework for automated STED super-resolution microscopy.Scientific reports · 2026Article
- Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.Small methods · 2026Review
- Advanced Imaging for Live-Cell Spatiotemporal Monitoring: Technologies and Applications.Research (Washington, D.C.) · 2026Review
- An enhanced mountain climbing search algorithm to enable fast and accurate autofocusing in high resolution fluorescence microscopy.Methods and applications in fluorescence · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The integration of Machine Learning (ML) with super-resolution microscopy represents a transformative advancement in biomedical research. Recent advances in ML, particularly deep learning (DL), have significantly enhanced image processing tasks, such as denoising and reconstruction. This review explores the growing potential of automation in super-resolution microscopy, focusing on how DL can enable autonomous imaging tasks. Overcoming the challenges of automation, particularly in adapting to dynamic biological processes and minimizing manual intervention, is crucial for the future of microscopy. Whilst still in its infancy, automation in super-resolution can revolutionize drug discovery and disease phenotyping leading to similar breakthroughs as have been recognized in this year's Nobel Prizes for Physics and Chemistry.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.