Securing AI in Healthcare: Protecting Patient Data With: Pelu Tran
Pelu Tran, CEO of Ferrum Health, addresses the pressing issue of patient data security in the era of AI. He explains why hospitals overwhelmingly prefer AI systems to run within their own controlled environments—either on-premises or in their own cloud—rather than in vendor-controlled clouds. Pelu outlines the risks of vendor environments, including breaches, unauthorized model retraining, and misuse of aggregated data. He also discusses the regulatory barriers, such as FDA requirements that limit adaptive model updates, and how new open-source models could reshape secure AI deployment in healthcare.
Episode Contents:
About the Guest
Pelu Tran is CEO of Ferrum Health, leading innovations in secure AI deployment for healthcare systems. Learn more at: https://www.linkedin.com/in/pelutran/
Key Takeaways
- Hospitals prefer AI tools deployed within their own secure environments
- Vendor cloud models risk breaches and unauthorized data use
- FDA regulations hinder continuous AI model updates
Transcript Summary
Can hospitals trust AI with sensitive patient data?
Hospitals prefer AI tools in their own environments to avoid security risks and unauthorized use.
Why avoid vendor-controlled clouds?
Vendors may breach agreements, retrain models without consent, and misuse aggregated data.
What about continuous model improvement?
FDA rules currently prevent many adaptive AI updates, limiting vendor claims of continuous learning.
More Topics
- AI in the Healthcare Industry
- AI and Medical Innovation
- Healthcare Ethics and Policy
- AI in Patient Care
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About the Series
AI and Healthcare—with Mika Newton and Dr. Sanjay Juneja is an engaging interview series featuring world-renowned leaders shaping the intersection of artificial intelligence and medicine.
Dr. Sanjay Juneja, a hematologist and medical oncologist widely recognized as “TheOncDoc,” is a trailblazer in healthcare innovation and a rising authority on the transformative role of AI in medicine.
Mika Newton is an expert in healthcare data management, with a focus on data completeness and universality. Mika is on the editorial board of AI in Precision Oncology and is no stranger to bringing transformative technologies to market and fostering innovation.