Integrating AI with Cellular and Mechanobiology: Trends and Perspectives

Mechanobiology explores how physical forces and cellular mechanics influence biological processes. This field has experienced rapid growth, driven by advances in high-resolution imaging, micromechanical testing, and computational modeling. At the same time, the increasing complexity and volume of mechanobiological imaging and measurement data have made traditional analysis methods difficult to scale. Artificial intelligence (AI) has emerged as a practical tool to address these challenges by providing new methods for interpreting and predicting biological behavior. Recent studies have demonstrated potential in several areas, including image-based analysis of cell and nuclear morphology, traction force microscopy (TFM), cell segmentation, motility analysis, and the detection of cancer biomarkers. Within this context, we review AI applications that either incorporate mechanical inputs/outputs directly or infer mechanobiologically relevant information from cellular and nuclear structure. This study summarizes progress in four key domains: AI/ML-based cell morphology studies, cancer biomarker identification, cell segmentation, and prediction of traction forces and motility. We also discuss the advantages and limitations of integrating AI/ML into mechanobiological research. Finally, we highlight future directions, including physics-informed and hybrid AI approaches, multimodal data integration, generative strategies, and opportunities for computational biophysics-aligned applications.