As artificial intelligence becomes increasingly embedded in medical devices and healthcare systems, the concept of data integrity is evolving rapidly. This webinar explores the intersection of cybersecurity, regulatory compliance, and AI-driven decision-making in the context of connected medical device data. Drawing from real-world experience and recent FDA guidance, our experts will discuss the foundational principles of data integrity accuracy, consistency, reliability, and traceability. As well as how they apply to modern healthcare technologies.
We’ll examine the challenges of implementing audit logs, the implications of AI hallucinations, and the regulatory distinctions between clinical support and clinical decision-making. The session will also reflect on historical cases that shaped today’s standards, including the Barr Decision and Able Labs scandal, and how lessons from the past inform current best practices.
Join us for a dynamic conversation between industry veterans as they unpack the ethical, technical, and regulatory dimensions of data integrity in the digital age.
Learning Objectives
By the end of this webinar, participants will be able to:
- Define the core principles of data integrity and explain their relevance in healthcare and medical device ecosystems.
- Differentiate between clinical support and clinical decision-making in AI applications and understand the regulatory implications of each.
- Identify key challenges in implementing audit logs and maintaining traceability in medical device data systems.
- Recognize the risks associated with AI hallucinations and black-box decision-making in clinical environments.
- Evaluate historical case studies (e.g., Barr Decision, Able Labs) to understand how data integrity standards have evolved.
- Apply a risk-based approach to data protection, focusing on critical data and regulatory compliance (HIPAA, FDA, GDPR).
- Discuss the importance of data pruning and anonymization in reducing exposure and enhancing cybersecurity.
