Multimodal artificial intelligence for predicting hair follicle survival and aesthetic outcomes following hair transplantation surgery

Background

Artificial intelligence (AI) has considerable potential to support outcome assessment in hair transplantation; however, its clinical reliability depends on the availability of complete, standardized, and appropriately linked patient-level data. Despite growing interest in AI-assisted aesthetic and clinical assessment, the readiness of currently reported hair-transplantation data for predictive and multimodal modelling remains insufficiently characterized.