The Transformative Role of Artificial Intelligence in Accelerating Biomedical Research: A Focus on Alzheimer's Disease

This article explores the transformative role of artificial intelligence in accelerating biomedical research, with a particular focus on Alzheimer's disease. The article examines how AI platforms have revolutionized traditional research methodologies through enhanced data processing capabilities, improved diagnostic accuracy, and accelerated drug discovery processes. The article highlights significant advancements in four key areas: AI-enabled research platforms and infrastructure, early detection and diagnostic applications, genomic analysis and target discovery, and autonomous research systems. These innovations have led to unprecedented improvements in processing complex datasets, identifying early disease markers, analyzing genetic variations, and automating research processes. The article demonstrates how AI integration has dramatically reduced research timelines while maintaining high accuracy rates across various applications, potentially transforming the future of biomedical research and therapeutic interventions.

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Publication Details

Journal
Journal of Computer Science and Technology Studies
Published
2025-05-14
DOI
https://doi.org/10.32996/jcsts.2025.7.4.44
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

The Transformative Role of Artificial Intelligence in Accelerating Biomedical Research: A Focus on Alzheimer's Disease

Poorani Subramanian
1 links
Journal of Computer Science and Technology Studies
Artificial Intelligence in Healthcare and Education
article

The Transformative Role of Artificial Intelligence in Accelerating Biomedical Research: A Focus on Alzheimer's Disease

Poorani Subramanian
article en

Abstract

This article explores the transformative role of artificial intelligence in accelerating biomedical research, with a particular focus on Alzheimer's disease. The article examines how AI platforms have revolutionized traditional research methodologies through enhanced data processing capabilities, improved diagnostic accuracy, and accelerated drug discovery processes. The article highlights significant advancements in four key areas: AI-enabled research platforms and infrastructure, early detection and diagnostic applications, genomic analysis and target discovery, and autonomous research systems. These innovations have led to unprecedented improvements in processing complex datasets, identifying early disease markers, analyzing genetic variations, and automating research processes. The article demonstrates how AI integration has dramatically reduced research timelines while maintaining high accuracy rates across various applications, potentially transforming the future of biomedical research and therapeutic interventions.

Journal of Computer Science and Technology StudiesVol. 7(4)
The University of Texas at Austin (US)
Openalex Percentile: Top 93%
Artificial Intelligence in Healthcare and Education
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