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  5. Labeling for Transparency and Trust in AI/ML-enabled Cardiac Devices and Software: Patient and Clinician Needs - 01/27/2026
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Webcast | Virtual

Event Title
Labeling for Transparency and Trust in AI/ML-enabled Cardiac Devices and Software: Patient and Clinician Needs
January 27, 2026

Date:
January 27, 2026
Time:
3:00 p.m. - 4:00 p.m. ET

Yale University/Mayo Clinic CERSI

Tuesday, Jan 27, 2026
3:00 – 4:00 PM (Eastern Standard Time)

Presented By

Barbara Barry, PhD

Barbara Barry, PhD
Human-Computer Interaction (HCI) researcher, Assistant Professor of Medicine, Yale University/Mayo Clinic

About the Presentation

Artificial intelligence (AI) and machine learning (ML) are increasingly embedded in cardiac monitoring and diagnostic devices, yet adoption depends on transparency and merited trust among clinicians and patients. This research explored what information users need to trust and accept AI/ML-enabled technologies in cardiology. We conducted a systematic literature review, focus groups with providers and patients, and a national survey to identify and validate core information elements for labeling AI/ML-based cardiac software and devices. Our review revealed key ethical concerns including privacy, security, and equity, and highlighted barriers and facilitators of trust, underscoring the need for transparency and oversight in AI-enabled cardiac care. Our patient focus group findings showed patient needs for 12 core elements of communication about the use of AI in cardiology. Survey findings revealed that communicating key elements, such as regulatory approval, device performance, provider oversight, and AI’s added value, significantly increased patient trust (14–19%) and intention to use (13–18%). Based on these insights, we developed conceptual label prototypes and an evidence-based framework for informed labeling to support regulatory science and human-centered adoption of AI in medicine. This work highlights the critical role of transparency and user-centered communication in the implementation of AI in healthcare.

About the Presenter

Barbara Barry, Ph.D., is a Human-Computer Interaction (HCI) researcher studying how interaction with artificial intelligence (AI) influences human cognition, communication, and behavior. Her work spans the development and evaluation of intelligent conversational agents, knowledge engineering to advance AI, and large-scale implementations of AI-powered technologies to improve health and education. She brings experience in human-centered innovation, including leadership in design strategy and user-focused approaches for emerging digital technologies. Her current research focuses on user experience and the integration of AI in medicine, including clinician- and patient-centered applications of predictive analytics, large language models (LLMs), and tools such as chatbots, clinical decision support systems, and automated summarization. She is particularly interested in the intersection of Human-AI Interaction, implementation science, and AI ethics. Dr. Barry is a faculty member in the Division of Health Care Delivery Research and a collaborative scientist at the Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery at Mayo Clinic. She also serves on Mayo Clinic’s Artificial Intelligence Bioethics Council and AI Translation Board. Dr. Barry earned her bachelor’s degree from Massachusetts College of Art and Design and her master’s and Ph.D. in Media Arts and Sciences from the Massachusetts Institute of Technology.

Remote Access Information

https://mc-meet.zoom.us/webinar/register/WN_8VgAp7uKSFWBf6rcWUF92Q

Advance Registration Required.

For Questions and Reasonable Accommodations

Please contact Ruize Li at Ruize.Li@fda.hhs.gov or go to Interpreting.Services@oc.fda.gov.

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