Abstract
Surgery is a real-time, embodied and safety-critical form of care in which decisions, anatomy, physical action and patient outcomes are tightly coupled. Artificial intelligence (AI) has improved the recognition of instruments, anatomy, workflow, technical performance and multimodal surgical context, but these capabilities have limited clinical value if they remain confined to retrospective benchmarks, narrow procedural settings and isolated perception tasks. The central challenge is to determine whether surgical models can support safer operative care under uncertainty, across procedures, institutions, devices and patient contexts, without obscuring surgical responsibility. This is not a scaling problem alone, but a problem of representation, evidence and accountability. Generalist surgical foundation models may provide a shared basis for clinically useful surgical understanding, but surgery demands a more constrained formulation than general biomedical AI, one that is anchored in operative accountability, real-time decision-making and patient-level consequences. Clinical-grade surgical intelligence should accordingly be defined as a qualification standard, not a capability threshold, for models intended to assist surgeons. First, surgery should be treated as a generalization problem across sensory-action regimes, longitudinal perioperative episodes and heterogeneous clinical environments. Second, model development should progress from machine-readable perception to temporally anchored procedural understanding, evidence-grounded reasoning, operating-room multimodal intelligence and surgeon-supervised action support. Third, clinical-grade claims should be earned through evidence that extends beyond benchmark accuracy to external and cross-regime validity, real-time reliability under intraoperative uncertainty, human-facing utility, patient-level relevance and lifecycle governance. We therefore reframe generalist surgical foundation models not as autonomous substitutes or increasingly capable pretraining assets, but as components of a clinically accountable intelligence layer for safer operative care. The clinical value of these models will depend on whether broader representations, stronger evidence and explicit accountability mature together.