Currently submitted to: Journal of Participatory Medicine
Date Submitted: Mar 6, 2026
Open Peer Review Period: Mar 20, 2026 - May 15, 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Warning: This is an author submission that is not peer-reviewed or edited.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 AI-Empowered Patient: A Framework for Responsible AI Engagement in Healthcare
ABSTRACT
Artificial intelligence (AI) is transforming healthcare delivery and patient engagement, as tools like diagnostic imaging algorithms, symptom checkers, and AI platforms such as ChatGPT Health, Claude, and Google Health AI proliferate. However, this rapid adoption has left patients surrounded by AI yet lacking the necessary frameworks and literacy to use these tools safely and effectively. This viewpoint proposes the AI-Empowered Patient™ framework—a patient-centered model built on three pillars (Preparation, Verification, Protection)—that empowers individuals to engage responsibly with healthcare AI. We further outline governance principles for patient-facing AI encompassing transparency, human oversight, privacy by design, equity and inclusion, and continuous monitoring, and introduce an AI Privacy and Operations model for operationalizing those principles at the organizational level. A comparative analysis of major AI healthcare platforms and targeted recommendations for healthcare organizations, policymakers, technology developers, and patients are also presented. AI will not replace physicians, but it will fundamentally reshape how patients experience care. Success requires active patient participation, robust organizational governance, and regulatory frameworks that prioritize trust, transparency, and equity.
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© 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.