Why Alzheimer’s Needs More Than Better Drugs

Alzheimer's disease exposes one of the greatest frustrations of modern medicine: despite decades of research, there is still no treatment that reliably alters its course. At best, existing therapies may slow decline temporarily in some patients. For millions of families, this reality is devastating.

Faced with a disease marked by uncertainty, slow progression, and high variability – and for which there is no cure – progress may depend less on perfecting drugs alone and more on improving how we detect early change and assess whether treatments are truly working.

Part of the challenge is structural. Evidence-based medicine is built on statistics and population-level evidence, while Alzheimer’s unfolds uniquely in every individual, shaped by biology, genetics, cognitive reserve, and time. Two patients with the same diagnosis may follow very different trajectories, respond differently to the same treatment, and decline at different rates. Yet therapeutic decisions continue to be guided by what worked on average in large clinical trials.

A new approach now challenges this logic. Known as “AI-enabled digital twins,” it uses advanced statistics and artificial intelligence to estimate what would likely have happened to a specific patient without a given intervention. Instead of asking whether a treatment works in general, this approach asks, “Is this treatment helping this person, right now?”

This shift – from averages to individual trajectories – is a change in mindset as much as in technology. AI-enabled digital twins do not promise cures. But in a disease fraught with uncertainty, slow change, and high variability, they offer context: a clearer way to interpret change, guide care, and judge whether an intervention truly justifies the burdens, costs, and hopes it carries.

Why Alzheimer’s Is So Hard To Treat

Alzheimer’s disease places medicine in an uncomfortable position. Diagnosis has become more precise, yet the ability to change the disease’s course remains limited. Most available therapies offer, at best, modest slowing of decline – and even that benefit is uneven.

The deeper issue is not only the absence of better drugs. It is how decisions are made.

Alzheimer’s is not a single disease unfolding along a predictable path. It reflects a convergence of biological processes, genetics, lifestyle factors, cognitive reserve, and underlying diseases. Two patients with the same diagnosis may decline at different rates, experience different symptoms, and respond differently to the same treatment.

Yet clinical decisions are still guided by results from large trials – what worked on average in thousands of patients. Necessary for approval, this logic becomes blunt when applied to individuals.

For clinicians and families alike, the uncertainty persists: "Is this treatment actually helping – or are we simply hoping it is?"

What AI-Enabled Digital Twins Actually Are

Digital twins may sound futuristic, but the idea behind them is straightforward.

They are not perfect replicas of a person. They are disciplined comparisons.

Using large datasets – including clinical records, cognitive assessments, imaging, wearable data, and demographic information – researchers build statistical models of patients who closely resemble a given individual in the factors known to influence disease progression. These models are then used to estimate what that person’s trajectory would likely look like without a specific intervention.

The value lies in the contrast.

Instead of observing decline and guessing whether a treatment is helping, clinicians can compare what is happening with what was likely to have happened otherwise. The question shifts from “Did this drug work in general?” to “Is this patient doing meaningfully better than expected?”

In a disease where change is slow and variability is high, that comparison matters.

Why This Changes Clinical Judgment

In Alzheimer’s care, small changes are easy to misread. Symptoms fluctuate. Decline can temporarily plateau. Expectations shape perception.

Digital twins help separate signal from noise. They provide a reference point that medicine has long lacked: a structured way to estimate the unseen alternative.

They do not replace clinical judgment. They sharpen it.

They also open practical possibilities – earlier recognition of ineffective treatments, clearer identification of who responds and who does not, and more efficient clinical trials that demand fewer patients and less time.

Above all, they bring care closer to reality: individual rather than abstract.

A Real-World Illustration: Learning From a Natural Experiment

This logic is not confined to algorithms.

In Wales, a public health cutoff for the shingles vaccine unintentionally created two nearly identical groups of older adults: those eligible for vaccination and those just beyond the age limit to qualify. The difference between them was not health behavior or awareness, but the timing of birth relative to the vaccine eligibility cutoff.

By following these groups over time, researchers could compare cognitive outcomes between people who were otherwise remarkably similar. Those who received the vaccine were less likely to develop cognitive impairment, less likely to be diagnosed with dementia, and – among those with dementia – less likely to die from it.

The study does not prove causation. The vaccine was never designed to protect the brain, and caution is warranted.

But the lesson is clear: when closely matched individuals differ by a single intervention, meaningful signals emerge.

Digital twins aim to make that kind of comparison intentional rather than accidental.

Final Thoughts

Alzheimer’s confronts medicine with its limits. When cures remain out of reach, progress is no longer defined by dramatic breakthroughs of novel drugs but by the quality of judgment applied along the way.

AI-enabled digital twins do not promise to solve the disease. What they offer is something more realistic – and more urgent: context. A clearer way to understand whether an intervention is truly helping a specific person, at a specific moment, along a specific trajectory.

Early versions of this approach are already being explored in leading academic centers, including Massachusetts General Hospital and the Mayo Clinic, primarily to support clinical decision-making and the design of more targeted trials.

In Alzheimer’s care, this shift – from treating averages to understanding individual trajectories – matters. When certainty is unattainable, medicine is measured less by what it promises than by how honestly it helps us decide – about treatment, burden, and time. Not better drugs alone, but better ways of knowing when they matter.