Jack Loughran Thu 15 Jan 2026
Collected at: https://eandt.theiet.org/2026/01/15/autonomous-ai-screens-cognitive-decline-using-routine-clinical-notes
An autonomous AI system capable of screening for cognitive decline using routine clinical documentation has been developed by researchers at the Boston-based Mass General Brigham.
The system, which requires no human intervention or prompting after deployment, achieved 98% specificity in real-world validation testing: that is, the rate at which it reduces false positives.
Corresponding author on the study Hossein Estiri said: “This AI system includes five specialised agents that critique each other and refine their reasoning, just like clinicians would in a case conference.”
Cognitive impairment remains significantly underdiagnosed in routine clinical care, and traditional screening tools and cognitive tests are highly resource-intensive to administer and difficult for patients to access.
The Alzheimer’s Society estimates that a third of people living with dementia in England and Northern Ireland, and nearly half of people living with dementia in Wales, don’t have a formal diagnosis.
Yet early detection has become increasingly critical, especially with the recent approval of Alzheimer’s disease therapies that are most effective when administered early in the disease.
“By the time many patients receive a formal diagnosis, the optimal treatment window may have closed,” said co-lead study author Lidia Moura.
To better capture at-risk patients, the team developed an AI system that runs on an open-weight large language model that can be deployed locally within hospital information technology infrastructure. The five agents work collaboratively to make clinical determinations and refine them to address errors and improve sensitivity.
These agents operate autonomously and collaborate to refine their detection capabilities until performance targets are met or the system determines it has converged. The researchers stressed that no patient data is transmitted to external servers or cloud-based AI services.
The study analysed more than 3,300 clinical notes from 200 anonymised patients at Mass General Brigham and was able to turn everyday documentation into a chance to screen for cognitive issues.
“Clinical notes contain whispers of cognitive decline that busy clinicians can’t systematically surface,” Moura said. “This system listens at scale.”
When the AI system and human reviewers disagreed, an independent expert re-evaluated each case. Among the disagreement cases, the expert validated the AI’s reasoning 58% of the time, meaning the system was often making sound clinical judgments that initial human review had missed.
“We expected to find AI errors. Instead, we often found the AI was making defensible judgments based on the evidence in the notes,” said Estiri.
Although the system achieved 91% sensitivity (the ability to detect true positives) under balanced testing, its sensitivity decreased to 62% under real-world conditions.
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