Patient-Centered Governance for Healthcare AI: The True Lifecycle Approach and the GCC Model for Global Standards

A “True Lifecycle Approach” for Governing Healthcare AI: Lessons from the GCC

Artificial intelligence (AI) is becoming an essential tool in healthcare, but current governance frameworks often fall short in protecting patients. This article presents a “True Lifecycle Approach” (TLA) to healthcare AI governance. Unlike existing models that focus mostly on technical risk and regulatory compliance, the TLA embeds core healthcare law principles—such as informed consent, liability, and patient rights—through every stage of AI’s development, approval, and use.

Why Current AI Governance in Healthcare Misses the Mark

Healthcare operates under strict legal and ethical standards designed to protect patients. These include confidentiality of medical data, informed consent, and the medical standard of care. However, many AI governance frameworks prioritize risk assessments and technical transparency without fully integrating these healthcare-specific safeguards. For example, an AI diagnostic tool might comply with regulations like the European Union’s AI Act but still conflict with established medical practices, potentially putting patients at risk without clear paths for recourse.

This gap creates challenges in accountability and trust. Patients need assurance that AI systems are developed with their safety and rights as a priority—not just technical compliance.

The Case for a True Lifecycle Approach

The TLA proposes embedding legal and ethical considerations into all phases of healthcare AI:

• Research and Development: Setting guidelines to ensure AI is designed with patient safety in mind from the outset.
• Market Approval: Integrating healthcare law principles in regulatory approvals beyond just device safety.
• Post-Implementation Governance: Ongoing oversight that ensures accountability, transparency, and respect for patient rights during AI use in healthcare.

This patient-centric approach treats patients as active stakeholders rather than passive recipients. It focuses on transparency, informed consent, and equitable care throughout the AI’s lifecycle.

Research and Development: Building on Strong Foundations

The earliest phase of AI development is critical for embedding healthcare values. For instance, between 2021 and 2024, a multidisciplinary team in Qatar developed Research Guidelines for Healthcare AI Development. These non-binding guidelines encourage best practices tailored to local healthcare contexts, including patient safety and ethical considerations.

The project also proposed a certification process for researchers who follow these guidelines, aiming to build trust among stakeholders that the AI systems meet essential standards before reaching patients.

Approval Stage: Beyond Device Safety

Once AI systems complete development and validation, they often require regulatory approval, especially if used for diagnosis or treatment decisions. However, many AI applications, such as administrative tools for scheduling or triage, fall outside traditional medical device regulations.

Guidelines like those from Qatar help fill this regulatory gap by ensuring all healthcare AI applications adhere to principles of fairness, accountability, and transparency. This includes documenting intended uses, limitations, and compliance with data protection laws.

Post-Implementation Governance: Ensuring Accountability in Practice

After deployment, continuous governance is essential. The Gulf Cooperation Council (GCC) countries offer practical models here. For example, Abu Dhabi and Dubai have implemented binding policies that complement earlier governance stages.

Centralized governance structures in the GCC, like Qatar’s Ministry of Communications and Information Technology and Saudi Arabia’s Data and AI Authority (SDAIA), facilitate oversight of AI’s ongoing use in healthcare. These frameworks aim to maintain patient safety, monitor performance, and uphold medical ethics throughout AI’s operational life.

Conclusion: A Patient-Centered Model for Healthcare AI

Current AI governance often overlooks crucial legal and ethical protections that healthcare demands. The True Lifecycle Approach addresses this by weaving patient rights and medical law into every stage of AI’s journey—from research to market approval and post-implementation oversight.

Examples from the GCC demonstrate how such an approach can work in practice, offering a more comprehensive, transparent, and accountable model. For healthcare professionals and policymakers, adopting a lifecycle mindset ensures AI serves patients effectively and ethically, building trust and safeguarding wellbeing.