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Review
. 2025 Jan 10:e2401757.
doi: 10.1002/smtd.202401757. Online ahead of print.

Self-Driving Microscopes: AI Meets Super-Resolution Microscopy

Edward N Ward  1 , Anna Scheeder  1 , Max Barysevich  1 , Clemens F Kaminski  1
Affiliations
Review

Self-Driving Microscopes: AI Meets Super-Resolution Microscopy

Edward N Ward et al. Small Methods. .

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.

Keywords: deep learning; machine learning; microscopy; super‐resolution.

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