AI models, bias and data sharing efforts to tackle Alzheimer's disease and related dementias

Artificial intelligence (AI), often seen as a harbinger of future innovation, also presents a dilemma: it can perpetuate existing human biases. However, this issue is not novel or unique to AI. Humans have long been the progenitors of biases, and AI, as a product of human creation, often mirrors these inherent tendencies. Here, we present a perspective on the development and use of AI, recognizing it as a tool influenced by human input and societal norms, rather than an autonomous entity. Modern efforts to technologically enabled data collection approaches and model development, particularly in the context of Alzheimer's disease and related dementias, can potentially reduce bias in AI. We also highlight the importance of data sharing from existing legacy cohorts to help accelerate ongoing AI model development efforts for greater scientific good and clinical care.

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Publication Details

Journal
The Journal of Prevention of Alzheimer s Disease
Published
2025-12-01
DOI
https://doi.org/10.1016/j.tjpad.2025.100400
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

AI models, bias and data sharing efforts to tackle Alzheimer's disease and related dementias

Vijaya B. Kolachalama, Rhoda Au, Vijay Sureshkumar
The Journal of Prevention of Alzheimer s Disease
Artificial Intelligence in Healthcare and Education
article

AI models, bias and data sharing efforts to tackle Alzheimer's disease and related dementias

Vijaya B. Kolachalama, Rhoda Au, Vijay Sureshkumar
article en

Abstract

Artificial intelligence (AI), often seen as a harbinger of future innovation, also presents a dilemma: it can perpetuate existing human biases. However, this issue is not novel or unique to AI. Humans have long been the progenitors of biases, and AI, as a product of human creation, often mirrors these inherent tendencies. Here, we present a perspective on the development and use of AI, recognizing it as a tool influenced by human input and societal norms, rather than an autonomous entity. Modern efforts to technologically enabled data collection approaches and model development, particularly in the context of Alzheimer's disease and related dementias, can potentially reduce bias in AI. We also highlight the importance of data sharing from existing legacy cohorts to help accelerate ongoing AI model development efforts for greater scientific good and clinical care.

The Journal of Prevention of Alzheimer s DiseaseVol. 13(1)
Boston University (US), University of Massachusetts Boston (US), BIO Ventures for Global Health (US)
Openalex Percentile: Top 69%
Artificial Intelligence in Healthcare and Education
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