Artificial intelligence and big data for precision regenerative medicine in knee osteoarthritis: endotyping, responder prediction, and clinical translation

Knee osteoarthritis (KOA) is a heterogeneous whole-joint disease, and regenerative and orthobiologic therapies such as platelet-rich plasma (PRP), mesenchymal stem cells (MSCs), bone marrow aspirate concentrate (BMAC), microfragmented adipose tissue (MFAT), and extracellular vesicles (EVs) show variable clinical effects. This variability reflects a dual heterogeneity: patients differ in structural damage, inflammation, metabolism, biomechanics, pain mechanisms, and molecular endotypes, while therapeutic products differ in composition, dose, viability, secretome, and manufacturing protocols. This Mini Review discusses how multimodal characterization of both patients and products may provide the data foundation for precision regenerative medicine in KOA. Imaging, radiomics, biomechanics, multi-omics, and product-quality attributes can be integrated to define meaningful endotypes and support responder prediction. We critically evaluate current artificial intelligence (AI) applications and demonstrate that, although AI has advanced automated imaging assessment and KOA progression prediction, direct evidence for regenerative treatment-response prediction remains scarce. Existing models are largely limited to PRP, whereas validated AI models for MSC-, BMAC-, MFAT-, and EV-based therapies are lacking. Clinical translation will require more than high discrimination metrics. Explainable AI, calibration, uncertainty estimation, external and prospective validation, standardized product reporting, and clinical decision support integration are essential. Future progress depends on matched patient-product-outcome cohorts that enable adaptive, explainable, and clinically actionable treatment selection.