Building trust in health AI starts with collaboration. CHAI’s Responsible AI Guide turns high-level principles into actionable recommendations - uniting clinicians, technologists, and patient advocates to shape transparent, ethical AI in healthcare. Presented by Merage Ghane, PhD, Director of Responsible AI at CHAI.
Abstract:
Although AI in healthcare depends on collaboration across disciplines, those involved in its development and implementation can operate in silos, having limited insight into one another’s practices. A shared framework is therefore important for harmonizing best practices across a growing range of specialties, interests, and concerns. Responsible AI entails a common understanding of both ethics and quality management principles, and it requires a shared translation of those principles into practical, transparent approaches to evaluating AI systems. This article examines the goals, challenges, and strategies for building consensus around AI guidelines in healthcare, drawing from the early experience of the Coalition for Health AI (CHAI). From 2023 to 2024, CHAI led a yearlong consensus building initiative to develop the Responsible AI Guide—a framework translating high-level principles into concrete recommendations (Elmore et al. Forthcoming). The effort drew survey insights from people across more than 100 organizations and convened over 60 experts for deliberation—including clinicians, patient advocates, regulators, data scientists, and healthcare administrators. These activities not only served to construct the Guide; they underscored the importance of inclusive, iterative consensus-building as the foundation of trustworthy AI in healthcare. In the absence of clear regulation, institution-level coalitions like CHAI play a vital role. By turning collective expertise into actionable recommendations, consensus-based frameworks can support accountability and continuous adaptation as AI and its care contexts evolve.
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Merage Ghane, Ph.D., is the Director of Responsible AI at the Coalition for Health AI (CHAI), where she leads efforts to develop guidelines and best practices for AI development and implementation in healthcare. In this role, she collaborates with a wide array of health industry stakeholders—including clinicians, technologists, policymakers, and patient advocates across multiple sectors—to develop practical products, tools, and services that prioritize responsible innovation and impact.
Merage is passionate about bringing people together to make translational and tangible contributions to the field of health AI, in service of greater mental, physical, and social health for all. Before joining CHAI, Merage was a Principal Behavioral Designer at ideas42, focusing on AI and ML in Health. She earned her Ph.D. in Clinical Science/Psychology from Virginia Tech, where she applied computational and machine learning methods to study how the brain processes uncertainty during decision-making. Dr. Ghane continued this research as a postdoctoral associate at the University of Pittsburgh’s Department of Psychiatry, investigating the clinical neuroscience of approach-avoidance behaviors and how individual differences in uncertainty processing affect these behaviors.