AI in Laboratory Informatics: Cutting Through the Hype
Some AI in labs is real and valuable. A lot is marketing language. Let's sort through what's actually happening.
Where AI Actually Delivers Value Today
91-98% accuracy on handwritten requisitions. 43.9% reduction in data entry time. Mature and valuable.
ML models identify orders likely to cause denials. Catches issues before processing starts.
Pattern matching against payer rules. Labs using intelligent validation see denial rates drop below industry average.
Computer vision detecting hemolysis, lipemia, icterus. Technology improving rapidly.
Where AI Falls Short (For Now)
Regulatory barriers, liability concerns, data requirements, context complexity. AI can flag unusual results, but humans make decisions.
Requires years of clean operational data most labs don't have. Rules-based monitoring works today; truly predictive AI is still emerging.
The 'lights-out lab' doesn't exist. Labs deal with endless exceptions requiring human judgment.
Red Flags When Evaluating "AI-Powered" Products
Questions to Ask Vendors
- 1What specific problem does the AI solve?
- 2What data was the model trained on?
- 3What's the accuracy in production (not idealized conditions)?
- 4How does it handle edge cases?
- 5What human oversight is required?
- 6How is the model updated and improved?
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