Advances in Quantum-Assisted AI for Alzheimer's disease: Methods, Metrics and Models
Abstract
Alzheimer's disease (AD), a progressive neurodegenerative disorder, presents significant challenges in early diagnosis and treatment. Advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), when combined with the power of computation of Quantum Computing (QC), offer promising avenues for the prediction and diagnosis of AD. This literature review surveys twenty research studies exploring the combination of QC with AI/ML/DL for AD prediction and compared through algorithms, evaluation metrics, time for training, datasets separately for ML and DL models. Challenges in current implementations and directions for research work are identified in review summary. The novelty of this review lies in its systematic comparison of quantum-assisted AI models across both machine learning and deep learning paradigms for Alzheimer’s detection, highlighting recent (2020–2024) research progress, performance benchmarks, and open challenges.
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