Governing Artificial General Intelligence in Future Healthcare Institutions: Risk-Adaptive Institutional Authorization for Clinical and Operational Authority
Year: 2026
Type: article
Abstract: Increasingly general and agentic artificial intelligence raises an institutional question of authority: which actions may a system perform, how may it combine them, and who may revise or withdraw permission? For prospective artificial general intelligence (AGI), cross-domain planning makes unfamiliar combinations of familiar tasks a central problem of authorization. This article develops a Risk-Adaptive AGI Governance Framework through conceptual synthesis, purposive document comparison, and three constructed scenarios. Four determinants—task risk, demonstrated capability, institutional preparedness, and accountability—inform five authorization gates. The governing rule grants the least authority sufficient for a justified service objective within mandatory constraints. Information, recommendation, and bounded execution permissions can be combined only through additional authorization of their dependencies. A versioned record binds component permissions, configuration, and cross-service ownership; changes to a shared condition reopen affected grants. Administrative scheduling illustrates professional and patient challenges to an otherwise technically eligible grant. Emergency coordination traces partial execution, shared infrastructure failure, unavailable fallback, and conditional restoration. Four testable propositions address risk-sensitive allocation, preparedness, calibrated reliance, and precommitted containment. RAGF connects established lifecycle safeguards to continuing institutional authorization, dispute resolution, and patient remedy. The proposed evaluation agenda examines safety, workflow benefit, equitable access, and the costs of maintaining and withdrawing permission. For future healthcare institutions, preparation for AGI entails an accountable allocation of discretion across services, professionals, and patients.
Source: AI & Future Society
Authors: Feifei Chen, Chengwen Song, Yuexin Lu, Qilong Wang
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7225779232