A new study, begun in the UK, is using AI to help improve dementia and Alzheimer’s diagnosis, aiming for a more accurate diagnosis achieved in a shorter amount of time. Currently, patients who display unclear symptoms can wait for years for a diagnosis.
The first clinic to start the study is the Aneurin Bevan University Health Board, in South-East Wales. Eventually, trials will be rolled out to reach 1000 patients across the UK. This British trial involves a simple blood test measuring p-tau217 to sharpen and speed Alzheimer’s diagnoses.
Administrative Efficiency
Hospitals are using AI to triage referrals, schedule scans, draft clinic letters, and surface missing test results so clinicians spend more time with patients. Whether it is healthcare triage, financial verification and compliance, or a digital services platform, the playbook is the same.
AI is also transforming other sectors that rely on fast and accurate decisions. In eCommerce, intelligent recommendation systems analyse browsing patterns and purchase histories to predict demand, personalise offers, and improve stock management. Offshore online casinos use similar technology to monitor player behaviour, detect suspicious activity, and adjust promotions or limits in real time. Both examples show how automation can make operations more adaptive and data-driven.
The same principle applies in healthcare. When AI manages background tasks and information flow, clinicians gain more time for observation and patient interaction. In dementia care, this focus is vital because personal attention and consistent human contact improve trust, comfort, and quality of life while technology handles the routine workload.
Medical Diagnostics
In these early rollouts, AI is already helping clinicians read images, lab results, and longitudinal patient data with greater speed and consistency. AI providing decision support for CT scans has also cut time to treatment and lifted recovery rates after nationwide rollout.
Meanwhile, NHS stroke centres also use AI to triage brain scans, with results delivered in minutes. AI’s application in medical diagnosis promises earlier clarity for families, faster routes to treatment, and fewer bottlenecks for already overstretched health services.
All this attests to the fact that assistive AI can change outcomes at scale. In dementia, the ADAPT blood-test study is testing whether adding an early p-tau217 result makes the diagnostic journey faster, cheaper, and fairer across regions.
Drug Discovery
AI models using machine learning (ML) can sift through chemical libraries and clinical data to identify and propose the most suitable candidates for clinical studies. They can also predict toxicity and repurpose known compounds.
In the identification of neurodegeneration, a shortened feedback cycle reduces the time between target hypotheses and small clinical signals. In turn, this helps teams prioritise what may move the needle for Alzheimer’s and related dementias.
Personalized Treatment Plans
From genetics to comorbidities, AI can assemble a patient-level profile, then suggest care pathways or trial eligibility with a clearer view of benefit–risk.
In memory services, that could mean earlier referrals to disease-modifying therapies or tailored support once a diagnosis is confirmed by imaging and blood biomarkers.
Predictive Analytics
With enough data and context, AI models can flag people with high-risk profiles long before crises hit. The goal with predictive analytics and AI’s integration in healthcare overall is proactive care and optimised systems. I.e., healthcare that prevents and performs optimally.
An AI model using predictive analytics, for example, could identify a stroke candidate who needs rapid transfer for thrombectomy before it becomes an emergency. Such a model could flag a patient who needs earlier cognitive assessment and blood testing. Potentially, this could save wasted time, expenses, and even reduce trauma.
Conclusion
Trials like the UK’s stroke rollout, the newly begun dementia trial rollout are but a few instances of the next wave of AI use cases in healthcare. All these systems are ultimately intended as assistive tools, aimed at shortening time-to-decision, standardizing quality between regions, and documenting why calls were made.
The UK stroke rollout has already shown how much of a difference it can make when AI results in improved healthcare. The dementia blood-test trial will be the next big proof point, especially if it delivers faster, fairer access to diagnosis across the NHS.
