A 2022–2025 method-comparative review of AI in neurodegenerative disease found that AI can support earlier risk stratification, disease characterization, and monitoring—when models are matched to the biological signal and validated rigorously, with performance driven more by data quality and study design than algorithmic complexity.

Why It Matters To Your Practice

• Neurodegenerative diseases are biologically heterogeneous, so “one-size-fits-all” imaging AI is unlikely to generalize across scanners, sites, and patient subtypes.

• The review emphasizes that apparent gains in neuroimaging AI often reflect better feature representation and validation rather than more complex architectures—directly affecting whether tools are trustworthy in clinic.

• Multimodal strategies (imaging plus molecular, EHR, speech/gait, or biosensors) are emerging as a pathway to more clinically meaningful characterization than imaging-only models.

Clinical Implications

• When evaluating an AD-focused neuroimaging AI tool, prioritize evidence of external validation, site/scanner robustness, and clinically realistic test sets over headline accuracy.

• Ask whether the model’s outputs are uncertainty-aware (e.g., calibrated probabilities, confidence intervals) to support safer decision-making and triage rather than binary “AD vs no AD” labeling.

• Favor approaches that provide interpretable links to neurobiology (e.g., region-level attributions tied to known AD patterns) to reduce the risk of learning non-biological shortcuts.

Insights

• Methodological transparency is positioned as a prerequisite for clinical readiness: clear cohort definitions, preprocessing, leakage prevention, and reporting of missingness and confounding.

• Model architecture should follow signal: different neuroimaging questions (early risk, subtype characterization, progression monitoring) may require different representations and endpoints.

• Next-generation paradigms (multimodal integration and newer AI frameworks) are discussed as methodological advances, but the review cautions against assuming immediate clinical deployability.

The Bottom Line

• For Alzheimer's Disease (AD) neuroimaging, the “best” AI approach is the one with high-quality data, biologically grounded features, and rigorous external validation—complexity alone doesn’t predict clinical value.