Human Review Requirements in Clinical AI: The MAGI Multipersona Framework

Background: Large language models can support clinical work, but their use in high-stakes settings raises risks of over-reliance, opacity, and erosion of professional judgment. Objective: To present MAGI, a conceptual multipersona framework for ethical deliberation and criticality triage in clinical decision support. Approach: The proposal distinguishes constrained analytical personas from autonomous clinical agents and combines legal, personalist bioethical, and biomedical perspectives through a content-analysis-informed synthesis. No empirical implementation or validation results are reported. Proposed framework: A coordinator is intended to document relevant considerations, agreements, disagreements, evidentiary gaps, and reasons for escalation. The proposed output is an auditable deliberation report, not a treatment recommendation. Clinician review is required before any use; high-criticality cases or persistent material disagreement require escalation to the appropriate accountable human process. No convergence threshold or iteration count is established as a safety criterion. Conclusion: A multipersona architecture may help make value conflicts and automation-related risks visible before action is taken. Prospective evaluation is required to determine whether it improves issue detection, appropriate reliance, usability, or safety compared with simpler alternatives.