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ViewAttendees 7

Medical Devices

Navigating the Challenges of Medical AI Usability

Thursday, May 7, 2026
16:45 - 17:45 WEST
Location: Auditorium 2
Learning Level: Intermediate
Description: The integration of Artificial Intelligence (AI) into medical devices creates new challenges for usability and human factors engineering. While AI promises unprecedented diagnostic and therapeutic capabilities, it introduces unique challenges in ensuring healthcare professionals can use these devices safely and effectively in complex clinical settings. This session moves beyond regulatory theory to provide a practical roadmap for navigating the critical intersection of AI, user experience (UX), and real-world clinical application.

This panel will dissect real-world case studies through a multidisciplinary lens, providing a holistic view of the challenges involved. Participants will gain actionable insights into addressing core usability-related challenges like algorithm explainability, automation bias, and cognitive load in high-stakes environments. You will learn practical methods for designing, evaluating, and validating AI-driven workflows, helping you generate the compelling evidence needed to demonstrate safety and effective use. Attendees will leave better equipped to guide their cross-functional teams, minimize use-related risks, and build a stronger foundation for successful product adoption and market access.

Learning Objectives:

  • Upon completion, participants will be able to describe the unique human factors and usability challenges introduced by AI-driven medical devices, such as automation bias, algorithm transparency, and managing the cognitive load on clinicians.
  • Upon completion, participants will be able to formulate strategies for designing and validating usability for AI-powered medical devices, focusing on how to generate compelling evidence that addresses unique risks and supports a successful regulatory submission.
  • Upon completion, participants will be able to critique and improve their organization's existing human factors processes to better accommodate the specific challenges of AI, ensuring a more robust approach to risk management and evidence generation.