摘要

Objectives: To deliver a standards-first, indication-driven framework that compares EV sources by clinical signal, safety / regulatory consideration, and manufacturability, supporting practical, brand-agnostic decisions for aesthetic dermatology. Introduction: Extracellular vesicles (EVs), including exosomes, are being increasingly explored for the treatment of photoaging, scarring dyschromia, and hair disorders. Yet, clinicians face heterogeneous sources, including human (mesenchymal, adipose, and platelet), animal, plant-derived, and synthetic (exosome mimetic) materials, uneven characterization, and unclear regulatory landscapes, which hinder standardized and unbiased selection in practice. Materials / method: A narrative systematic review ( 2019-2025), minimum MISEV-compliant characterization (e.g, CD9 /63/81, TEM/NTA, functional assays), and quantitative outcomes in skin & hair indications. Evidence was mapped by indication and route (topical, post-laser/needling, injectable). We then built a multi-criteria decision analysis (MCDA) weighting: clinical evidence (20%), safety/regulatory risk (20%), scalability/GMP (15%), MISEV quality (10%), stability/formulation (5%), cost/access (5%), and biological indication-fit (25%). Results: Human adipose/MSC EVs show the strongest clinical signal for post-procedure rejuvenation and acne scarring, early data support androgenetic alopecia. Milk-derived EVs have favorable delivery and scalable manufacture with preliminary results. Plant EV-like nanoparticles offer antioxidant/photoprotective effects but lack standardization. Engineered/synthetic vesicles enable targeting and payload control, yet are still in their early stages. MCDA yields a brand-agnostic, indication-first matrix by endpoint (texture/scar, dyspigmentation, photoaging, hair density). Conclusion: A MISEV-aligned MCDA harmonizes heterogeneous EV sources into a transparent, evidence-weighted tool for aesthetic dermatology. Adoption of rigorous reporting and awareness of current safety advisories/regulatory status enhances translational quality while minimizing bias.