Researchers have utilised artificial intelligence to re-evaluate data from a concluded clinical study for an Alzheimer’s medication, uncovering fresh insights that could significantly improve future drug development. The AI model determined that the medicine reduced cognitive decline by 46% in patients with early-stage, slowly developing moderate cognitive impairment, a condition frequently preceding Alzheimer’s disease.

Utilising AI, researchers categorised trial participants according to the velocity of their condition’s progression: either sluggish or rapid. This segmentation allowed for a more exact analysis of the drug’s effect within each group.

This strategy of selecting trial participants with greater precision could enhance the probability of identifying individuals who will derive the most benefit from a treatment, potentially decreasing both the duration and expense of new drug development by optimising the design and efficiency of clinical trials.

An AI model created by researchers at the University of Cambridge forecasts the probability and rate at which individuals in the initial phases of cognitive decline may advance to full-blown Alzheimer’s disease. The researchers asserts that the model exhibits threefold more accuracy compared to traditional clinical evaluations, which depend on memory assessments, MRI imaging, and blood biomarkers.

Employing this AI-driven patient classification, researchers re-evaluated data from a previous clinical trial that initially indicated no overall medication efficacy. While the medicine effectively eliminated beta amyloid—a characteristic protein associated with Alzheimer’s—in all patients, only those in the early, slow-progressing cohort exhibited symptomatic improvement. This indicates that the medicine may be advantageous, but solely for a particular subset of patients.

These findings highlight the significance of employing AI to identify patient subgroups, enabling researchers to more accurately tailor therapies to individuals most likely to benefit, hence accelerating the development of viable treatments.

The results were disseminated today in Nature Communications.

Professor Zoe Kourtzi, the principal author of the report and a researcher in the Department of Psychology at the University of Cambridge, stated: “Promising new drugs fail when given to people too late, when they have no chance of benefiting from them. With our AI model we can finally identify patients precisely, and match the right patients to the right drugs. This makes trials more precise, so they can progress faster and cost less, turbocharging the search for a desperately-need precision medicine approach for dementia treatment.”

She also explained:
“Our AI model gives us a score to show how quickly each patient will progress towards Alzheimer’s disease. This allowed us to precisely split the patients on the clinical trial into two groups – slow, and fast progressing, so we could look at the effects of the drug on each group.”

The AI-driven approach is now being translated into real-world clinical care with support from Health Innovation East England, the NHS’s innovation agency in the region.

Joanna Dempsey, Principal Advisor at Health Innovation East England, said:
“This AI-enabled approach could have a significant impact on easing NHS pressure and costs in dementia care by enabling more personalised drug development – identifying which patients are most likely to benefit from treatment, resulting in faster access to effective medicines and targeted support for people living with dementia.”

It’s important to note that these drugs are not cures. The aim is to slow the rate of cognitive decline, allowing patients to retain their abilities for longer.

Dementia is the leading cause of death in the UK and a major global health burden. It costs $1.3 trillion annually, and the number of cases is expected to triple by 2050. Despite extensive investment—over $43 billion—research efforts have faced high failure rates, with more than 95% of clinical trials for dementia treatments proving unsuccessful. One of the major challenges has been the diversity in symptoms, disease progression, and treatment responses across individuals.

While some dementia drugs have been approved in the US, concerns over side effects and lack of cost-effectiveness have stalled their introduction into the NHS.

Understanding the natural variability in how individuals experience Alzheimer’s is essential to developing more effective, personalized treatments. Though several drugs are available, their benefits vary widely between patients.

Professor Kourtzi emphasized the role AI can play in changing this trajectory:
“AI can guide us to the patients who will benefit from dementia medicines, by treating them at the stage when the drugs will make a difference, so we can finally start fighting back against these cruel diseases. Making clinical trials faster, cheaper and better, guided by AI has strong potential to accelerate discovery of new precise treatments for individual patients, reducing side effects and costs for healthcare services.”

She added a personal note on the urgency of this work:
“Like many people, I have watched hopelessly as dementia stole a loved one from me. We’ve got to accelerate the development of dementia medicines. Over £40 billion has already been spent over thirty years of research and development – we can’t wait another thirty years.”

Original Publication
Journal: Nature Communications
DOI: 10.1038/s41467-025-61355-3
Subject of Research: People
Article Title: AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer’s Disease clinical trial
Article Publication Date: 17-Jul-2025