True agentic AI in healthcare requires three essential components: a high-level goal, system integration with access to data and tools for smart rule creation, and self-monitoring capabilities for corrections. Currently, most systems labeled as 'agentic' are actually just complex if-then workflow rules that humans must manually adjust, lacking the autonomous decision-making and self-correction abilities that define genuine agentic AI.
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The AI Is Smarter Than My 1990s Hospital Infrastructure — So Why Isn't It Running the System?Added:
You gave I think one of the most precise definitions of agentic AI that I have encountered and you described it as a system that pursues a defined goal, selects actions based on outputs and feedback [music] and then adjust its behavior. Now that's a system level view that most seale leaders haven't yet operationalized. Have you used any AI agents in radiology and is there anything that is compelling? I think we're still very early in agentic use.
Most people aren't using it. Everyone's talking about it, but frankly, when I hear people talking about it, those sort of three things that I use to define it.
One, is it being given a highle goal?
Two, is it integrated into the system so it has access to different pieces of data and tools so it can enable like smart rule creation based on that goal?
And three, can it monitor itself [music] to make corrections? And those things just don't really exist right now in healthcare. So I think a lot of people are talking about it and using it as a buzzword, but it's not really truly agentic. I think what people are calling [music] agentic is really more workflow rules that that can get pretty complex, but they're really [music] a constellation of if then rules that a human has to change. And if they act the same way [music] regardless of this what's happening in the system, then it's not agentic. Catch Nina Cotler on the Chief EI Officer podcast by Knowledge Networks.
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