Do Hospitals Struggle with AI Integration? With: Pelu Tran
Pelu Tran, CEO of Ferrum Health, outlines why AI adoption in hospitals remains slow despite the technology’s readiness. The barriers lie in integrating modern, cloud-based AI into legacy systems, navigating multimillion-dollar onboarding processes, and addressing strict patient data governance. Tran warns that most AI tools underperform in real-world conditions and require continuous monitoring for bias, drift, and workflow impact. He advises hospitals to view AI as a lifecycle rather than a point solution, building governance frameworks to manage performance, safety, and cost over time.
Episode Contents:
About the Guest
Pelu Tran is the CEO of Ferrum Health, helping hospitals vet, deploy, and govern AI tools for safer, more effective healthcare. Learn more at: https://www.linkedin.com/in/pelutran/
Key Takeaways
- Hospitals face high costs and complex governance to deploy AI.
- AI models must be monitored for bias, drift, and safety.
- Point solutions can lead to unsustainable AI support costs.
Transcript Summary
Why is AI still so hard to implement in hospitals?
Hospitals use legacy systems with limited IT capacity. AI tools are modern, cloud-based, and data-intensive, creating integration and governance hurdles.
What are the main risks?
AI bias, performance drift, and safety issues. Most models underperform in real-world use compared to FDA clearance.
How should hospitals approach AI adoption?
Treat AI as a lifecycle with governance for performance, bias, and workflow impact. Avoid costly point solutions that hinder scaling.
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.