Currently submitted to: JMIR AI
Date Submitted: Apr 9, 2026
Open Peer Review Period: Apr 14, 2026 - Jun 9, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
The Empowerment Imperative: Reframing Generative AI Integration in Clinical Workflows from Risk Avoidance to Physician Usability
ABSTRACT
Background:
Generative artificial intelligence (GenAI) holds transformative potential for healthcare delivery, yet its integration into active clinical workflows remains limited. The dominant discourse focuses on risk mitigation—hallucinations, liability, regulatory uncertainty, and potential bias—while a more proximate constraint receives comparatively little attention: the persistent failure to engineer GenAI tools that genuinely empower physicians in daily practice.
Objective:
This perspective article argues that the primary bottleneck impeding GenAI adoption in healthcare is not technical risk but clinical usability—a phenomenon we term the 'empowerment gap.' We propose a seven-level Empowerment Maturity Framework (EMF) to characterize current deployment readiness and identify the structural dimensions along which progress is needed.
Methods:
Generative AI adoption in clinical settings, physician workflow integration, ambient documentation outcomes, clinical decision support, and EHR usability.
Results:
Healthcare stakeholders must pivot from a risk-centric to an empowerment-centric paradigm. Engineering GenAI into seamless, reliable, context-aware clinical workflows is the binding constraint on adoption. Solving usability will generate the real-world use, data, and feedback cycles necessary for trustworthy AI integration. Empowerment engineering must precede, not follow, risk management.
Conclusions:
Healthcare stakeholders must pivot from a risk-centric to an empowerment-centric paradigm. Engineering GenAI into seamless, reliable, context-aware clinical workflows is the binding constraint on adoption. Solving usability will generate the real-world use, data, and feedback cycles necessary for trustworthy AI integration. Empowerment engineering must precede, not follow, risk management.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.