Accountability for large language models in health care.

Large language models are entering clinical workflows faster than health-care institutions can assign responsibility for their failures. This situation is creating governance gaps with consequences that extend beyond individual patients to public health systems. The result is an accountability vacuum in which responsibility for harm mediated by large language models can be spread across clinicians, institutions and vendors. This responsibility gap distributes harm inequitably, particularly in low- and middle-income countries where regulatory infrastructure for digital health is still developing. We propose earned delegation as a predeployment standard: authority should be delegated only when evidence is proportionate to clinical risk, substantive human oversight is integrated and resourced, and responsibility for foreseeable failure modes is assigned in advance. To operationalize this standard, health systems should adopt a predeployment accountability charter specifying intended use, excluded use, validation evidence, oversight design, escalation pathways, auditability, subgroup performance review and named accountable parties. The charter should be integrated into existing institutional review, accreditation and procurement structures. Earned delegation provides a governance framework that health ministries, regulators and institutional leaders can adopt to ensure that use of large language models serves public health goals without outpacing them.