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

Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.

• Level A · Stronger Clinical Evidence
• Level B · Emerging clinical evidence with positive signals
• Level C · Early human research exploring benefits
• Level D · Scientific groundwork from lab and animal studies
• Emerging · Emerging topic under active research

Abstract (original English)

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

Evidence level

Evidence from laboratory and animal studies provides groundwork for understanding mechanisms and potential before human studies continue.

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