  Objectives: The objectives of this narrative review are to summarize the
current state of AI applications in neuroimaging for early Alzheimer's disease
(AD) prediction and to highlight the potential of AI techniques in improving
early AD diagnosis, prognosis, and management.
  Methods: We conducted a narrative review of studies using AI techniques
applied to neuroimaging data for early AD prediction. We examined
single-modality studies using structural MRI and PET imaging, as well as
multi-modality studies integrating multiple neuroimaging techniques and
biomarkers. Furthermore, they reviewed longitudinal studies that model AD
progression and identify individuals at risk of rapid decline.
  Results: Single-modality studies using structural MRI and PET imaging have
demonstrated high accuracy in classifying AD and predicting progression from
mild cognitive impairment (MCI) to AD. Multi-modality studies, integrating
multiple neuroimaging techniques and biomarkers, have shown improved
performance and robustness compared to single-modality approaches. Longitudinal
studies have highlighted the value of AI in modeling AD progression and
identifying individuals at risk of rapid decline. However, challenges remain in
data standardization, model interpretability, generalizability, clinical
integration, and ethical considerations.
  Conclusion: AI techniques applied to neuroimaging data have the potential to
improve early AD diagnosis, prognosis, and management. Addressing challenges
related to data standardization, model interpretability, generalizability,
clinical integration, and ethical considerations is crucial for realizing the
full potential of AI in AD research and clinical practice. Collaborative
efforts among researchers, clinicians, and regulatory agencies are needed to
develop reliable, robust, and ethical AI tools that can benefit AD patients and
society.
