WELCOME to mHealthLAB

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.