AI-Empowered Mechanomedicine for Cancer-Related Lymphedema

Cancer-related lymphedema is a chronic progressive side effect of cancer treatments followed by lymph node dissection or radiotherapy. Clinicians often identify lymphedema through limb swelling, while the disease begins earlier than that. Lymphatic injury is the original cause, where elevated interstitial fluid pressure and distorted tissue mechanics will lead to immune activation and fibrofatty remodeling. Recently, mechanobiology studies have deepened our understanding by linking lymph stasis to T helper 2/transforming growth factor β signaling, fibroblast mechanotransduction, and YAP/TAZ activity that together lock tissues into a stiff, poorly draining state. Simultaneously, emerging artificial intelligence (AI) in the field are being explored, from proof-of-concept image classification to much more diagnostic models that integrate elastography, indocyanine green lymphography, radiomics, clinical variables, and wearable signals to detect preclinical mechanical signatures and predict risk. These advances are driving the development of promising mechanomedical approaches, such as adaptive compression systems, AI-assisted plans for lymphatic reconstruction, anti-fibrotic strategies, and lymphangiogenic regeneration, although most remain at preclinical or early clinical feasibility stages of translation. We discuss the strength of current evidence, challenges for clinical translation, and standards for reporting. We propose that cancer-related lymphedema can be understood as a measurable and targetable mechano-immune-fibrotic disease, for which AI may eventually support earlier diagnosis, risk prediction, and personalized mechanotherapy.