Researchers in the US have developed an agentic AI that can screen for people in the early stages of cognitive impairment by sifting through routine clinical documents.
The team from Mass General Brigham say the AI is fully autonomous, requiring no human intervention after it is deployed, and has shown 98% specificity in real-world validation testing.
They have published two large language model (LLM) workflows for the AI approach in Nature's npj Digital Medicine journal.
In the paper, they point to the potential for LLMs to "revolutionise clinical workflows by systematically processing and interpreting the complex narrative threads woven throughout medical documentation."
The study analysed more than 3,300 clinical notes produced during regular healthcare visits, from 200 anonymised patients, for signs of cognitive decline.
The AI agents' conclusions were reviewed by humans, and where there was disagreement, an independent expert stepped in with a re-evaluation.
The system achieved 91% sensitivity – the ability to correctly find cases – under balanced testing, but that fell to 62% under real-world conditions.
On the other hand, specificity – ruling out negative cases – was near-perfect.
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