April Session: PXI: From Principles to Collaboration to Impact: How Specialty Societies Can Advance Patient-Centered AI-PXI: From Principles to Collaboration to Impact: How Specialty Societies Can Advance Patient-Centered AI

PXI: From Principles to Collaboration to Impact: How Specialty Societies Can Advance Patient-Centered AI

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Video Summary

The webinar, moderated by Melissa West of the American Society of Nephrology, focused on how specialty societies can advance patient-centered AI through the CMSS AI/ML Task Force and the National Health Council’s PXI (Patient Experience and Innovation) Center. Randy Ruda explained that PXI was created to ensure patients are not left at the margins as AI and machine learning rapidly reshape healthcare. The center serves as a learning lab, a matchmaking hub for patient-tech collaborations, and a project incubator, with a broad alliance of patient organizations, professional societies, and industry partners.<br /><br />Sue Sheridan of Patients for Patient Safety U.S. emphasized that AI should be viewed through the lens of safety, diagnosis, equity, and outcomes that matter to patients. She described a personal experience where ChatGPT helped her identify Bell’s palsy quickly enough to get timely treatment, illustrating both AI’s promise and the mixed reactions patients may face when using it. She argued that patients are already using AI “at the speed of desperation,” especially when access to care is limited, and called for better training, safer tools, and shared access to evidence-based platforms.<br /><br />The discussion covered privacy, prompting, bias, and the need for clinicians and patients to use AI together with humility and transparency. Both speakers stressed the importance of engaging patients in guideline development, research, governance, and system design. They also urged specialty societies to help democratize access to trustworthy AI, collect patient-use data, and ensure AI supports—not replaces—patient-clinician partnership.

Keywords

patient-centered AI

specialty societies

machine learning

patient experience

healthcare innovation

AI safety

equity in healthcare

patient engagement

clinical guidelines

trustworthy AI