Executive Summary 

Healthcare has entered a new phase of artificial intelligence adoption. What began as experimentation is quickly becoming enterprise deployment, with AI supporting clinical care, administrative operations, cybersecurity, patient engagement, and business decision-making. As organizations expand their use of AI, the conversation is shifting from implementation to trust. 

What makes an AI system trustworthy? Who defines responsible AI? How should healthcare organizations prepare for evolving expectations from regulators, boards, clinicians, and patients? 

In the final session of Clearwater’s 2026 Healthcare AI Executive Summer Series, Baxter Lee, President of Clearwater, moderates a discussion with Brenton Hill, Head of Operations and General Counsel at the Coalition for Health AI (CHAI), and Julie Chua, Director of the Applied Cybersecurity Division at the National Institute of Standards and Technology (NIST). Together, they explore how voluntary frameworks, industry collaboration, and established risk management principles are helping shape the future of trustworthy AI in healthcare. 

The discussion emphasizes that trustworthy AI is not achieved through regulation alone. It is built through governance, transparency, accountability, cybersecurity, and continuous risk management. Healthcare organizations that invest in these capabilities today will be better prepared to scale AI responsibly as technology and regulatory expectations continue to evolve. 

Why This Conversation Matters 

Healthcare organizations are no longer asking whether AI will become part of healthcare. That question has already been answered. 

The challenge now is determining how organizations can deploy AI responsibly while maintaining the trust of patients, clinicians, regulators, and leadership. 

Unlike many emerging technologies, AI affects nearly every aspect of healthcare. It influences clinical decision-making, operational efficiency, documentation, research, cybersecurity, and patient engagement. As its role expands, healthcare leaders need governance models that support innovation while protecting patient safety, privacy, and organizational integrity. 

Throughout the discussion, the speakers emphasize that trustworthy AI is not defined by any single organization or regulation. Instead, it is the result of collaboration between healthcare providers, technology companies, standards organizations, government agencies, and industry leaders working toward common principles. 

Key Insights from the Discussion 

Healthcare Is Moving Beyond AI Pilots 

The panel begins by recognizing how quickly healthcare has progressed from AI experimentation to enterprise adoption. 

Many organizations have moved beyond isolated proof-of-concept projects and are now evaluating AI across multiple departments and business functions. AI is becoming integrated into clinical documentation, patient communications, operational workflows, cybersecurity platforms, and decision support systems. 

This shift changes the governance conversation. 

Organizations are no longer evaluating one AI application at a time. They are building long-term governance programs capable of supporting AI across the enterprise. 

The discussion reinforces that governance should mature alongside adoption instead of reacting after AI has already become deeply embedded within organizational operations. 

Trustworthy AI Is Built Through Governance 

One of the central questions explored during the session is deceptively simple: 

What makes AI trustworthy? 

The speakers explain that trustworthy AI is not defined by a single technology, certification, or regulation. 

Instead, trust is established through governance practices that promote: 

• Transparency  

• Accountability  

• Risk management  

• Human oversight  

• Cybersecurity  

• Privacy protection  

• Continuous monitoring  

Organizations earn trust by demonstrating that AI systems are implemented responsibly, evaluated consistently, and governed throughout their lifecycle. 

As Brenton Hill explains during the discussion, trustworthy AI depends on repeatable governance processes that remain effective even as technologies continue evolving. 

The Role of CHAI 

Brenton Hill discusses the mission of the Coalition for Health AI (CHAI) and the organization’s efforts to bring together healthcare providers, technology developers, policymakers, researchers, and industry leaders. 

Rather than developing regulations, CHAI focuses on building consensus around practical governance principles that healthcare organizations can begin applying today. 

The conversation highlights the importance of collaboration across the healthcare ecosystem. 

AI governance cannot be developed independently by technology companies, regulators, or providers alone. 

Meaningful progress depends on organizations sharing expertise, identifying best practices, and working toward common standards that support safe and responsible AI adoption. 

NIST Provides a Risk Management Foundation 

Julie Chua explains how the National Institute of Standards and Technology (NIST) contributes to AI governance through voluntary risk management frameworks. 

Rather than prescribing specific technologies, NIST provides organizations with practical guidance for identifying risk, evaluating governance maturity, improving decision-making, and establishing repeatable governance processes. 

The discussion reinforces an important point. 

Healthcare organizations do not need to wait for AI-specific regulations before improving governance. 

Many existing cybersecurity and enterprise risk management principles already provide an excellent foundation for governing AI technologies. 

Organizations that already practice disciplined risk management are well positioned to extend those capabilities into AI governance. 

Governance Should Support Innovation 

A recurring theme throughout the webinar is that governance should enable innovation, not restrict it. 

Healthcare organizations are adopting AI because they want to improve patient care, reduce administrative burden, strengthen operational efficiency, and support clinicians with better technology. 

Governance creates the structure needed to pursue these goals responsibly. 

Instead of asking whether governance slows innovation, the panel encourages organizations to view governance as the process that makes sustainable innovation possible. 

Organizations that build trust through accountability and transparency will ultimately be better positioned to adopt AI at scale. 

Trust Requires Continuous Improvement 

The discussion concludes by emphasizing that trustworthy AI is not a destination. 

AI technologies will continue evolving. 

New regulations will emerge. 

Organizational priorities will shift. 

Governance programs must adapt alongside these changes. 

Healthcare organizations should continuously review AI inventories, reassess risks, evaluate governance processes, and refine policies as technologies mature. 

Trust is maintained through continuous oversight rather than one-time approvals. 

Organizations that treat governance as an ongoing organizational capability will be better prepared for the future of healthcare AI. 

Practical Recommendations for Healthcare Leaders 

Throughout the discussion, the panel offers several practical recommendations: 

• Begin building AI governance before regulations require it.  

• Establish governance processes based on transparency and accountability.  

• Build on existing cybersecurity and enterprise risk management practices.  

• Participate in industry collaboration whenever possible.  

• Continuously monitor AI technologies throughout their lifecycle.  

• Design governance programs that can adapt as technology evolves.  

• View governance as an enabler of responsible innovation rather than a compliance exercise.  

These recommendations help organizations build trust while preparing for the next generation of AI adoption. 

Notable Quotes 

Brenton Hill: Trustworthy AI depends on governance processes that evolve alongside technology. 

Julie Chua: Existing risk management principles provide a strong foundation for governing AI. 

Baxter Lee: Healthcare is moving beyond AI experimentation toward enterprise adoption, making governance more important than ever. 

Questions This Discussion Answers 

• What is trustworthy AI in healthcare?  

• Who defines trustworthy AI?  

• What role does the Coalition for Health AI (CHAI) play?  

• How does NIST support AI governance?  

• How can healthcare organizations build trust in AI?  

• What governance principles support responsible AI adoption?  

• How can organizations balance innovation with accountability?  

• Why is collaboration important for the future of healthcare AI?  

About This Webinar 

This webinar concludes Clearwater’s 2026 Healthcare AI Executive Summer Series with a forward-looking discussion on the future of trustworthy AI in healthcare. Baxter Lee moderates a conversation with Brenton Hill of the Coalition for Health AI (CHAI) and Julie Chua of NIST on the governance frameworks, risk management principles, and collaborative efforts shaping responsible AI adoption. Drawing on perspectives from healthcare, industry, and government, the panel explains how organizations can establish governance programs that promote transparency, accountability, and trust while supporting continued innovation. The discussion provides healthcare executives, governance leaders, technology professionals, and policymakers with practical guidance for preparing their organizations for the next phase of AI adoption in healthcare.