Artificial intelligence is increasingly positioned within medication-management activities such as prescription screening, risk prioritization, information generation, treatment selection, and clinical decision support. Yet the continued presence of a pharmacist or another healthcare professional does not necessarily constitute meaningful human oversight. A person may be formally “in the loop” while lacking the competence, time, information, organizational support, or authority required to identify and correct a hazardous output. This article develops a proposed Oversight-Capability Framework for determining when human involvement in AI-assisted medication management may be considered meaningful. The framework defines oversight through four proposed functions: detecting a reason for scrutiny, interpreting and challenging the AI-assisted output, deciding whether the output should be accepted, modified, deferred, rejected, or escalated, and implementing or coordinating an appropriate response. These functions depend on four enabling conditions—competence, time, information, and authority—and may operate prospectively, continuously, when triggered, or retrospectively. Meaningful oversight is therefore treated as a demonstrable capability to alter a potentially hazardous medication trajectory rather than as a professional title, explanation display, alert acknowledgment, or nominal override option. The framework separates model output from medication decision, technical performance from clinical usefulness, automation from professional authority, and implementation from validated benefit. Evaluation should examine erroneous recommendations, disagreement, intervention feasibility, workload, subgroup harm, escalation, and organizational learning. The proposed framework is conceptual and requires validation across medication tasks, technologies, populations, professional roles, and organizational settings. It is not a clinical recommendation, regulatory standard, validated safety mechanism, or deployment-ready protocol.