Rockin' HIT Sales Podcast
Standards That Scale: Why AI Measurement and Trustworthiness Matter in Healthcare
Ram D. Sriram, Chief of the Software and Systems Division, National Institute of Standards and Technology’s Information Technology Laboratory (ITL)
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Episode Summary
In this episode of Rockin’ HIT Sales, David Hacker sits down with Ram D. Sriram of NIST for a practical conversation on why trustworthy AI in healthcare requires much more than a strong model or compelling demo.
Ram explains why measurement matters, what “metrology for AI” actually means, and why standards, interoperability, and uncertainty quantification are becoming essential for any Health IT company trying to earn trust and scale in clinical or operational settings.
The discussion also explores how startups should think about existing standards, what the NIST AI Risk Management Framework looks like in healthcare terms, and why knowing when a model does not know can be just as important as accuracy itself.
If you build, sell, or invest in healthcare AI, this episode is a practical look at what real-world readiness requires beyond product claims.
What You’ll Hear in This Episode
- Why AI measurement and calibration matter in healthcare
- What “trustworthy AI” looks like beyond buzzwords
- How standards can accelerate adoption instead of slowing innovation
- Why interoperability remains foundational in multi-source healthcare environments
- How uncertainty quantification changes clinical risk and decision pathways
- What early-stage companies should understand before putting AI in front of clinicians or patients
3 Brief Takeaways
About the Guest
Ram D. Sriram is Chief of the Software and Systems Division in the Information Technology Laboratory at NIST. He has worked across multiple eras of AI development and brings a long-range perspective on knowledge-based systems, neural networks, standards, interoperability, and trustworthy AI.
Transcript
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