Abstract
Augmented reality head-mounted devices (AR HMDs) are increasingly deployed in healthcare. Given the stringent safety and efficacy requirements of medical settings, proactive quantitative testing of key performance attributes prior to deployment is critical for risk assessment. A systematic performance evaluation framework is essential not only to support clinical adoption but also to secure regulatory approval. This review systematically summarizes hardware, software, and usability assessment methods for AR HMDs in healthcare, analyzes current research and experimental designs, and identifies challenges arising from device heterogeneity, limited coupling with real-world clinical scenarios, and subjective bias. To address these issues, we propose five design principles to guide the development of objective and practical evaluation methods: (1) identify key components based on core functions; (2) prioritize testing by functional contribution; (3) replicate authentic clinical and human-visual conditions; (4) objectify subjective perception; (5) test functionally linked components jointly.