The earliest signs of cognitive decline often appear not in a formal diagnosis, but in the small clues buried in health care providers’ notes.
A new study published Jan. 7 in the journal npj Digital Medicine suggests artificial intelligence (AI) can help identify these early signals — such as issues with memory and thinking or changes in behavior — by scanning doctor’s notes for patterns of concern. These might include recurring mentions of cognitive changes or confusion from the patient, or worries mentioned by family members attending the appointment with their loved one.
“The goal is not to replace clinical judgment but to function as a screening aid,” study co-author Dr. Lidia Moura, an associate professor of neurology at Massachusetts General Hospital, told Live Science. By highlighting such patients, she said, the system could help clinicians decide which people to follow up with, especially in settings where specialists are in short supply.
Whether that kind of screening actually helps patients depends on how it is used, said Julia Adler-Milstein, a health informatician at the University of California, San Francisco who was not involved in the study. “If the flags are accurate, go to the right person on the care team and are actionable, meaning they lead to a clear next step, then yes, they can be easily integrated into the clinical workflow,” she told Live Science in an email.
A team of AI agents, not just one
To build their new AI system, the researchers used what they call an “agentic” approach. This term refers to a coordinated set of AI programs — five, in this case — that each have a specific role and review one another’s work. Together, these collaborating agents iteratively refined how the system interpreted clinical notes without human input.
The researchers built the system on Meta’s Llama 3.1 and gave it three years of doctors’ notes to study, including clinic visits, progress notes and discharge summaries. These came from a hospital registry and had already been reviewed by clinicians who noted whether cognitive concerns were present in a given patient’s chart.
The team first showed the AI a balanced set of patient notes, half with documented cognitive concerns and half without, and let it learn from its mistakes as it tried to match how clinicians had labeled those records. By the end of that process, the system agreed with the clinicians about 91% of the time.
The finalized system was then tested on a separate subset of data that it hadn’t seen before, but that was pulled from the same three-year dataset. The second dataset was meant to reflect real-world care, so only about one-third of the records were labelled by clinicians as showing cognitive concern.
In that test, the system’s sensitivity fell to about 62%, meaning it missed nearly four in ten cases clinicians had marked as positive for signs of cognitive decline.
At first glance, the drop in accuracy looked like failure — until the researchers reexamined the medical records that the AI and human reviewers had classified differently.
Clinical experts reviewed these instances by rereading the medical records themselves, and did so without knowing whether the classification had come from clinicians or the AI. In 44% of cases, these reviewers ultimately sided with the system’s assessment rather than the original chart review conducted by a doctor.
“That was one of the more surprising findings,” said study co-author Hossein Estiri, an associate professor of neurology at Massachusetts General Hospital.
In many of those cases, he said, the AI applied clinical definitions more conservatively than doctors did, declining to flag concerns when notes didn’t directly describe memory problems, confusion or other changes in how the patient was thinking — even if a diagnosis of cognitive decline was listed elsewhere in the record. The AI was trained to prioritize mentions of potential cognitive concerns, essentially, which doctors might not always flag as important in the moment.
The results highlight the limits of manual chart review by doctors, Moura said. “When the signals are obvious, everyone sees them,” she said. “When they’re subtle, that’s where humans and machines can diverge.”
Karin Verspoor, a researcher in AI and health technologies at RMIT University who was not involved in the study, said the system was evaluated on a carefully curated, clinician-reviewed set of doctors’ notes. But because the data came from a single hospital network, she cautioned that its accuracy may not translate to settings where documentation practices differ.
The system’s vision is limited by the quality of the notes it reads, she said, and that constraint that can be addressed only through optimizing the system across diverse clinical settings, she argued.
Estiri explained that, for now, the system is intended to run quietly in the background of routine doctors’ visits, surfacing potential concerns alongside an explanation of how it reached them. That said, it is not yet being used in clinical practice.
“The idea is not that doctors are sitting there using AI tools,” he said, “but that the system provides insight — what we’re seeing, and why — as part of the clinical record itself.”
Tian, J., Fard, P., Cagan, C. et al. An autonomous agentic workflow for clinical detection of cognitive concerns using large language models. npj Digit. Med. 9, 51 (2026). https://doi.org/10.1038/s41746-025-02324-4
