Artificial Intelligence-Guided Extracellular Vesicle Platforms for Diagnosis and Treatment of Chronic Kidney Disease: A Scoping Review and Future Directions

Background and Objectives: Chronic kidney disease (CKD) affects roughly 788 million adults worldwide according to the Global Burden of Disease (GBD) 2023 estimate. Extracellular vesicles (EVs) are promising sources of biomarkers and therapeutic carriers, while artificial intelligence (AI) offers tools for high-dimensional EV analysis and computational design. Materials and Methods: We conducted a scoping review using a structured PubMed/MEDLINE search, run on 7 September 2026, and PRISMA-ScR reporting principles. Eligible studies applied AI/ML to EV-derived or closely related kidney-disease data. The review was not prospectively registered. The formal scoping evidence set was used to map kidney EV–AI studies focused on diagnosis, prognosis, and biomarker discovery; EV engineering, therapeutic design, manufacturing, and physiologically based pharmacokinetic (PBPK) modeling were synthesized separately as contextual and future-oriented literature identified through non-systematic contextual literature identification. Predictive models were critically appraised using a PROBAST-informed framework. Results: Nine reports were retained in the formal PRISMA evidence set. Direct kidney EV–AI evidence was limited and heterogeneous, with small cohorts and variable validation strategies. Several studies reported encouraging discrimination, but external validation, calibration, standardized EV workflows, and prospective clinical utility were uncommon. Contextual literature suggests potential roles for AI in EV engineering, manufacturing, and PBPK-informed therapeutic development, but these applications remain investigational in CKD. Conclusions: AI–EV integration is a promising research framework rather than a clinically validated platform. Translation will require standardized EV methods, adequately powered multicentre studies, independent validation, clinically relevant operating thresholds, prospective utility studies, and explicit manufacturing and regulatory strategies.