A review highlighted a central hurdle for industry-built surgical AI systems: independent validation and trustworthiness. The piece focuses on algorithms that interpret surgical video in real time—recognizing instruments, identifying anatomy, tracking procedural steps, and anticipating events—but argues the field’s defining test remains whether systems can be independently and reliably trusted. The reporting describes how commercialization is moving AI from laboratory benchmarks into real operating environments, where variation in technique, imaging devices, labeling, and workflow can stress model performance. Without external validation, adoption may stall despite promising early results. For developers and health systems, the near-term question is procedural rather than theoretical: how to prove robustness and safety across sites, surgeons, and hardware configurations through validation frameworks that can be audited.