Artificial Intelligence and Machine Learning in AFM-Based Nanomechanical Biomarkers: From Force Curves and Stiffness Maps to Disease Classification and Treatment Monitoring

Atomic force microscopy (AFM) has emerged as a powerful platform for quantifying nanoscale mechanical properties of cells, tissues, and extracellular matrix (ECM) components, providing candidate biomarkers for disease diagnosis, classification, prognosis, and treatment monitoring. However, the clinical translation of AFM-based nanomechanical biomarkers remains limited by low throughput, operator dependence, complex force-curve interpretation, heterogeneous biological samples, and the lack of standardized analytical pipelines. Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used to address these limitations by enabling automated AFM image and force-curve analysis, multiparametric feature extraction, cell and tissue classification, quality control, and high-throughput mechanophenotyping. This review summarizes how AI and ML have already been applied to AFM-derived nanomechanical and morphological data, with emphasis on cancer, fibrotic disease, and treatment response monitoring. We discuss classical ML models, deep learning approaches, clustering, fuzzy logic methods, and emerging automated Bio-AFM workflows. We further highlight current limitations, including small datasets, limited external validation, lack of reproducible reporting standards, and insufficient integration with clinical metadata. Finally, we propose a roadmap for AI-enabled AFM mechanobiomarkers, focusing on standardized datasets, explainable models, multimodal mechano-optical imaging, and clinically relevant validation strategies.