Governing Artificial General Intelligence in Future Healthcare Institutions: Risk-Adaptive Institutional Authorization for Clinical and Operational Authority
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Authors
Wuhan Donghu University
Kookmin University
Xi'an Medical University
Gansu University of Traditional Chinese Medicine
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.
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References
Bovens, M. (2007). Analysing and assessing accountability: A conceptual framework. European Law Journal, 13(4), 447–468. https://doi.org/10.1111/j.1468-0386.2007.00378.x
European Parliament & Council of the European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union, L, 2024/1689. https://eur-lex.europa.eu/eli/reg/2024/1689/oj
Freeman, S., Wang, A., Saraf, S., Potts, E., Mckimm, A., Coiera, E., & Magrabi, F. (2026). From principles to practice: An actionable framework for AI governance in healthcare organisations. BMJ Digital Health & AI, 2(1), Article e000018. https://doi.org/10.1136/bmjdh-2026-000018
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias: A systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19(1), 121–127. https://doi.org/10.1136/amiajnl-2011-000089
Goh, E., Gallo, R., Hom, J., Strong, E., Weng, Y., Kerman, H., Cool, J. A., Kanjee, Z., Parsons, A. S., Ahuja, N., Horvitz, E., Yang, D., Milstein, A., Olson, A. P. J., Rodman, A., & Chen, J. H. (2024). Large language model influence on diagnostic reasoning: A randomized clinical trial. JAMA Network Open, 7(10), Article e2440969. https://doi.org/10.1001/jamanetworkopen.2024.40969
Hager, P., Jungmann, F., Holland, R., Bhagat, K., Hubrecht, I., Knauer, M., Vielhauer, J., Makowski, M., Braren, R., Kaissis, G., & Rueckert, D. (2024). Evaluation and mitigation of the limitations of large language models in clinical decision-making. Nature Medicine, 30(9), 2613–2622. https://doi.org/10.1038/s41591-024-03097-1
Joint Commission & Coalition for Health AI. (2025). Guidance on the responsible use of AI in healthcare (RUAIH). https://digitalassets.jointcommission.org/api/public/content/dcfcf4f1a0cc45cdb526b3cb034c68c2
Lekadir, K., Frangi, A. F., Porras, A. R., Glocker, B., Cintas, C., Langlotz, C. P., Weicken, E., Asselbergs, F. W., Prior, F., Collins, G. S., Kaissis, G., Tsakou, G., Buvat, I., Kalpathy-Cramer, J., Mongan, J., Schnabel, J. A., Kushibar, K., Riklund, K., Marias, K., . . . Starmans, M. P. A. (2025). FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ, 388, Article e081554. https://doi.org/10.1136/bmj-2024-081554
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, 30(3), 286–297. https://doi.org/10.1109/3468.844354
Saltzer, J. H., & Schroeder, M. D. (1975). The protection of information in computer systems. Proceedings of the IEEE, 63(9), 1278–1308. https://doi.org/10.1109/PROC.1975.9939
Sittig, D. F., & Singh, H. (2010). A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Quality and Safety in Health Care, 19(Suppl. 3), i68–i74. https://doi.org/10.1136/qshc.2010.042085
Song, C., Hu, Y., Chen, F., Lenz, S., Li, J., Feng, Y., Zeng, X., & Wu, M. (2026). When algorithms become sacred: A cognitive-psychological model of trust, authority, and meaning in AI interaction. Journal of Social Cognition and Communication, 1(1), 22–31. https://doi.org/10.63802/jscc.V1.I1.291
Tabassi, E. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). National Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1
Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., Liu, X., Mateen, B. A., Mathur, P., McCradden, M. D., Morgan, L., Ordish, J., Rogers, C., Saria, S., Ting, D. S. W., . . . the DECIDE-AI expert group. (2022). Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nature Medicine, 28(5), 924–933. https://doi.org/10.1038/s41591-022-01772-9
Wang, A., Freeman, S., & Magrabi, F. (2026). Governance for safe and responsible AI in healthcare organisations: A scoping review of frameworks. npj Digital Medicine, 9(1), Article 516. https://doi.org/10.1038/s41746-026-02679-2
Wang, Q., Ma, S., Sun, J., Qi, X., Zhao, J., Ma, D., Liu, X., An, Q., Zhao, D., & He, S. (2026). Information quality, readability, and empathy of AI-generated public mental health information: A comparative evaluation of eight large language models. Frontiers in Public Health, 14, Article 1859078. https://doi.org/10.3389/fpubh.2026.1859078
Wells, B. J., Nguyen, H. M., McWilliams, A., Pallini, M., Bovi, A., Kuzma, A., Kramer, J., Chou, S.-H., Hetherington, T., Corn, P., Taylor, Y. J., Cuison, A., Gagen, M., Isreal, M., & FAIR-AI Consortium. (2025). A practical framework for appropriate implementation and review of artificial intelligence (FAIR-AI) in healthcare. npj Digital Medicine, 8(1), Article 514. https://doi.org/10.1038/s41746-025-01900-y
World Health Organization. (2021). Ethics and governance of artificial intelligence for health: WHO guidance. https://www.who.int/publications/i/item/9789240029200
World Health Organization. (2024). Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models. https://www.who.int/publications/i/item/9789240084759
You, J. G., Hernandez-Boussard, T., Pfeffer, M. A., Landman, A., & Mishuris, R. G. (2025). Clinical trials informed framework for real world clinical implementation and deployment of artificial intelligence applications. npj Digital Medicine, 8(1), Article 107. https://doi.org/10.1038/s41746-025-01506-4
Zhang, L., Wölflein, G., Ferber, D., Liang, J., Carrero, Z. I., Wu, X., Vibert, J., Clusmann, J., Möhrmann, L., Möhrmann, E. E., Wichmann, C., Wolf, F., Lenz, T., & Kather, J. N. (2026). On-premise medical AI agents for reliable clinical decision-making. Nature Medicine, 32(10), 3705–3716. https://doi.org/10.1038/s41591-026-04609-x
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This conceptual article draws on published literature cited in the reference list. No new empirical datasets were generated or analysed.
This work is licensed under a Creative Commons Attribution 4.0 International License.