The Problem of Data Decay in Healthcare With: Jason Alan Snyder
Jason Alan Snyder breaks down the challenges of poor data quality in healthcare, particularly the issue of data decay. He explains that while AI models depend on accurate and relevant data, much of today's healthcare information is outdated, incomplete, or biased, leading to flawed recommendations. Snyder identifies four main types of data decay—temporal, structural, contextual, and semantic—and highlights the risks of semantic manipulation in shaping medical terminology for market advantage. He advocates for clean, consent-based, and personally controlled data as the foundation for ethical, precise, and effective healthcare AI.
Episode Contents:
About the Guest
Jason Alan Snyder is a futurologist, inventor, and technologist with deep expertise in AI, data systems, and ethical innovation. Learn more at: https://www.evilrobot.com/
Key Takeaways
- Data decay undermines healthcare AI accuracy.
- Four main decay types: temporal, structural, contextual, semantic.
- Clean, consent-based data ownership is key for precision medicine.
Transcript Summary
How good is current healthcare data?
Most healthcare data is poor in quality and subject to decay, making AI models prone to bias and error.
What is data decay?
The gradual loss of accuracy, relevance, and usefulness due to outdated information, format changes, context shifts, and evolving terminology.
How can we improve healthcare data quality?
By ensuring clean data, true consent, personal control, and ethical use to build accurate, beneficial digital health models.
More Topics
- AI in Patient Care
- AI in the Healthcare Industry
- AI and Medical Innovation
- Healthcare Ethics and Policy
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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.