AI in Atomic Force Microscopy: Advancing Biological Nanoscale Imaging and Autonomous Discovery

2026-04-23

PRE

Seungmin Lee

Seokbeom Roh

Hyowon Woo

Gyudo Lee

Hyo Gi Jung

Ki-Baek Lee

Hakho Lee *

Dae Sung Yoon *

Jeong Hoon Lee *

DOI：10.1021/acsnano.6c02895

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Atomic force microscopy (AFM) enables label-free nanoscale imaging and nanomechanical profiling but remains constrained by low throughput, operator dependence, and variability in data interpretation. Artificial intelligence (AI) transforms AFM into a scalable and adaptive platform. Initially applied in materials science for super-resolution imaging, tip deconvolution, segmentation, and force-curve analysis, AI approaches are now being extended to biological AFM. These methods support robust denoising of soft matter maps, automated recognition of heterogeneous structures, and three-dimensional reconstruction of biomolecular assemblies. This review provides an end-to-end workflow of AI-enabled AFM─from probe optimization and adaptive control to multimodal data integration─highlighting advances relevant to mechanobiology and biomedical engineering. By surveying studies with amyloid fibrils, extracellular vesicles, membranes, and living cells, we show how AI–AFM convergence enhances reproducibility, throughput, and clinical utility. AI-driven AFM is poised to enable disease modeling, therapeutic screening, and precision diagnostics, establishing itself as a next-generation tool for biomedical discovery.

Keywords:

Atomic force microscopy

Cells

Nanoscale

Neural networks

Peptides and proteins

atomic force microscopy

artificial intelligence

machine learning

deep learning

biological imaging

nanomechanics

force spectroscopy

multimodal translation

signal interpretation

autonomous microscopy

AI in Atomic Force Microscopy: Advancing Biological Nanoscale Imaging and Autonomous Discovery

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