Sanjeevi Bharadwaj, Naveen Jeyaraman, Subasri Balasubramanian, Arulkumar Nallakumarasamy, Sathish Muthu, Madhan Jeyaraman
AI-assisted orthobiologics can be considered as a translational precision-medicine architecture, not a fully formed product. Standardized characterization of the biologic, a prospectively validated prediction model, easily understandable outputs, regulatory alignment, and ongoing monitoring of outcomes before routine clinical use are all necessary for safe implementation.
INTRODUCTION: The use of orthobiologics such as autologous peripheral blood-derived orthobiologics, bone marrow-derived biologics, adipose tissue-derived biologics, mesenchymal stem cells, and extracellular vesicles is gaining traction in the field of orthobiology for the treatment of osteoarthritis (OA), tendinopathy, cartilage injuries, and delayed musculoskeletal healing. Clinical responses are inconsistent due to biological variation among patients, disease manifestations, product composition, delivery accuracy, and outcome definitions.
MATERIALS AND METHODS: The literature was searched in PubMed, Embase, Cochrane Library, and Scopus databases from January 2019 to June 2026, and landmark and regulatory concepts were considered. The search terms were orthobiologics, platelet-rich plasma, mesenchymal stromal cells, OA, tendinopathy, artificial intelligence (AI), machine learning, imaging biomarkers, ultrasound guidance, responder prediction, potency assays, software as a medical device and regulation. The synthesis of evidence was done in a narrative fashion based on the Scale for the Assessment of Narrative Review articles principles.
RESULTS: AI has the greatest clinical relevance as an enabler for precision orthobiologics. Supervised models can predict responder probability after platelet-rich plasma; unsupervised clustering can identify inflammatory, metabolic, structural, or pain-dominant phenotypes; deep learning can quantify imaging biomarkers; natural language processing can identify longitudinal outcomes; and privacy-preserving learning can facilitate multicenter validation. Product variability may be lessened through parallel advances in cytometry, secretome profiling, potency testing, and image-guided delivery. However, the evidence is still early, largely retrospective, and prone to bias, data drift, poor external validation, and unclear regulatory classification.
CONCLUSION: AI-assisted orthobiologics can be considered as a translational precision-medicine architecture, not a fully formed product. Standardized characterization of the biologic, a prospectively validated prediction model, easily understandable outputs, regulatory alignment, and ongoing monitoring of outcomes before routine clinical use are all necessary for safe implementation.