The New Frontier in Alzheimer’s Prevention: AI-Powered Neurodiagnostic Solutions
Contributed Commentary by Jin Hyung Lee, PhD, Stanford University
July 18, 2025 | Imagine sitting down for a routine checkup and learning you could prevent one of the most devastating diseases of our time before it ever occurs. More than 12 million Americans could be living with Alzheimer’s disease by 2050, per the Alzheimer's Association. For many families, diagnosis arrives only after noticeable confusion or memory loss disrupts daily life. By that point, much of the brain has already been damaged, and options for slowing decline are limited.
This timeline raises questions worth asking: what if we could detect Alzheimer’s long before symptoms appear? Could early brain monitoring give people years—maybe decades—of better cognitive health? In this article, we’ll explore how emerging advances in brain signal analysis and artificial intelligence are moving that possibility closer to reality.
From Late Confirmation to Early Action
Traditional tools for diagnosing Alzheimer’s (such as MRI scans or memory tests) are designed to confirm that something has already gone wrong. They’re often expensive, hard to access, and built around identifying the already-established disease rather than catching its earliest signals.
That’s starting to change. New approaches are shifting the focus away from confirming decline and toward identifying the first signs of trouble. Among these, an unlikely candidate is gaining momentum: electroencephalography, or EEG.
EEG: A Closer Look at Brain Signals
EEG has been around for nearly a century. It records the brain’s electrical activity through electrodes placed on the scalp. Unlike MRI, EEG is portable, relatively inexpensive, and can be performed in almost any clinical setting. Historically, though, EEG wasn’t typically viewed as a practical option for early Alzheimer’s detection. The signals it records are complicated, with patterns that hint at neurodegeneration can be buried under layers of normal electrical activity.
For years, no one had a reliable way to pick out those hidden patterns. That’s no longer the case.
How Artificial Intelligence Changes the Equation
Algorithms trained on thousands of EEG datasets are now capable of spotting subtle, consistent markers linked to early cognitive decline. These predictive patterns, known as digital biomarkers, can appear years before someone shows outward signs of memory loss.
AI doesn’t just speed up analysis; it makes it possible. Human specialists can only review so much data at once. Machines, by contrast, can process enormous volumes of information to detect correlations too faint for the human eye. The idea is simple: if clinicians can spot signs of Alzheimer’s in the earliest stages, they have more time to help patients take meaningful steps to protect brain health through lifestyle changes, cognitive training, or emerging therapies.
A More Accessible Path to Brain Health
For many communities, especially in rural areas, access to brain imaging has always been limited. MRI and PET scans are expensive and require specialized facilities. EEG, combined with AI, offers a more practical alternative.
Envision a future where seniors can receive routine brain screenings in their primary care clinic. The process could resemble a standard checkup: electrodes applied in a matter of minutes, readings analyzed by AI, results reviewed in real time. Early detection can also help reduce the burden on caregivers and the broader healthcare system. When Alzheimer’s is caught in its earliest stages, families have more time to plan for the future, explore treatment options, and adjust daily routines while their loved one can still participate in decision-making.
By intervening sooner, families might avoid the high costs, repeated hospital visits, and emotional strain that so often accompany late-stage diagnosis. Widespread early screening could also ease pressure on long-term care facilities and create a more sustainable approach to supporting the millions of people expected to be diagnosed in the coming decades.
Looking Ahead: What Could the Next Decade Bring?
Within five to 10 years, it’s possible that routine brain monitoring will be as common as cholesterol checks. Mobile EEG units could visit community centers, senior living facilities, or even schools to monitor brain health as part of standard preventive care. There are already pilot programs exploring how portable EEG can track cognitive function over time. Some researchers believe these efforts could lead to large-scale databases that further refine AI’s ability to detect risk factors before damage accumulates.
Of course, no technology is a cure-all. Still, a model that emphasizes early awareness rather than crisis response could give people a better chance to plan, adjust, and protect their quality of life.
A Call to Rethink Early Detection
If we keep relying on tools designed only to confirm disease after it takes hold, millions more will face the same late-stage diagnosis that so many families know too well. It’s time to consider new strategies that make early screening affordable and practical. EEG and AI are already demonstrating that it’s possible to read the brain’s earliest warnings. The next step is to bring those tools into everyday care. By shifting focus to prevention, we stand to change what an Alzheimer’s diagnosis means—not an ending, but the start of more years lived with clarity, independence, and connection.
Dr. Jin Hyung Lee is the Founder of LVIS Corporation and an Associate Professor of Neurology & Neurological Sciences, Neurosurgery, Bioengineering, and (by courtesy) Electrical Engineering at Stanford University. Her lab bridges biology and engineering to advance novel brain-health diagnostics and therapies. Dr. Lee can be reached at ljinhy@stanford.edu.