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Related Concept Videos

Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

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Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
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Alzheimer's Disease: Overview01:26

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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
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Related Experiment Video

Updated: Jan 9, 2026

The Immersive Cleveland Clinic Virtual Reality Shopping Platform for the Assessment of Instrumental Activities of Daily Living
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Solving the 'Goldilocks problem' in dementia clinical trials with multimodal AI.

Andrew E Welchman1, Zoe Kourtzi2

  • 1Prodromic Ltd, Milton Hall, Ely Road, Milton, Cambridge CB24 6WZ, UK.

The Journal of Prevention of Alzheimer'S Disease
|December 1, 2025
PubMed
Summary

Artificial Intelligence (AI) can improve Alzheimer's Disease and related dementias (ADRD) clinical trials by identifying the right patients. This precision approach enhances treatment effectiveness and accelerates the development of new therapies for dementia care.

Keywords:
Alzheimer's diseaseClinical trialsCognitionEarly predictionImagingMultimodal AI

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Area of Science:

  • Neuroscience
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Alzheimer's Disease and related dementias (ADRD) therapeutics development faces challenges due to patient heterogeneity and diagnostic limitations.
  • Current clinical trial designs struggle to select patients who will benefit from novel treatments, impacting success rates.

Purpose of the Study:

  • To explore how Artificial Intelligence (AI), specifically multimodal machine learning, can address patient stratification challenges in ADRD.
  • To demonstrate AI's potential in optimizing clinical trial patient selection and enabling precision treatment in real-world settings.

Main Methods:

  • Examined the conceptual framework for identifying dementia stage and subtype.
  • Reviewed data from Alzheimer's disease therapeutic clinical trials.
  • Discussed the integration of AI into clinical workflows, model interpretability, generalizability, and ethical considerations.

Main Results:

  • AI-guided patient stratification can significantly improve clinical trial outcomes by ensuring appropriate patient inclusion.
  • AI can reduce trial costs and enhance patient recruitment efficiency.
  • Intelligent analytics, combined with scientific and clinical expertise, can accelerate diagnostic and therapeutic discovery.

Conclusions:

  • AI offers a powerful solution to the 'Goldilocks problem' in clinical trials, enabling precision medicine for ADRD.
  • Integrating AI into healthcare workflows is crucial for transforming dementia care and improving patient outcomes globally.
  • Addressing algorithmic bias and ensuring model generalizability are critical for the ethical and effective deployment of AI in dementia treatment.