Sustaining AI Development in Healthcare: Rethinking Rules and Responsibilities With: Dr. Nigam Shah
Dr. Nigam Shah, Co-founder of Atropos Health and Chief Data Scientist at Stanford Healthcare, examines the sustainability challenges in AI development for healthcare. He explains that while AI in medicine has existed for decades, the current academic-centric development practices are not suited for scaling from research to real-world applications. The conversation highlights the cost, time, and regulatory complexities, as well as the need for localized and continuous model validation to maintain performance.
Episode Contents:
About the Guest
Dr. Nigam Shah is Co-founder of Atropos Health and Chief Data Scientist at Stanford Healthcare. Learn more at: https://www.linkedin.com/in/nigam/
Key Takeaways
- Academic AI development practices are not scalable to real-world healthcare needs
- Stakeholder roles must be clearly defined for AI governance
- Continuous local validation is critical for sustainable AI deployment
Transcript Summary
Are current AI developments in healthcare sustainable?
No. Academic methods for model validation are too slow and costly for scaling. We need new rules for real-world use.
What governance is needed for AI in healthcare?
Shared responsibility across government, providers, vendors, insurers, and end users.
How do we maintain AI effectiveness over time?
Implement continuous local validation and monitoring to detect performance drift.
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.