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April 10, 2026Journal of Mental Health and Human BehaviourOpen Access

Artificial Intelligence for Predicting Mild Cognitive Impairment to Alzheimer’s Disease Conversion: A Systematic Review of Advances, Challenges, and Clinical Implications

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Key result

Deep learning outperforms traditional machine learning in predicting MCI-to-AD conversion, reaching ~0.92 AUC.

Why the study?

Timely prediction of mild cognitive impairment to Alzheimer's disease conversion allows earlier interventions, but traditional methods lack sensitivity while artificial intelligence models offer improved accuracy.

Do artificial intelligence models improve the prediction of mild cognitive impairment to Alzheimer's disease conversion compared to traditional methods?

Comparison

AI-based approaches utilizing neuroimaging, biomarkers, or multimodal frameworks

Design

Systematic review following PRISMA standards

Authors

DGDharmendra K. GuptaACArunima Chaudhuri

Discussion

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Member takes

Overview

May improve risk stratification for at-risk MCI patients; extends AI evidence but leaves open generalizability pending external validation.

Key Points

  • This review assesses AI methods that predict the conversion from mild cognitive impairment to Alzheimer's disease.
  • Conducted a systematic review following PRISMA guidelines.
  • Included peer-reviewed studies published between January 2020 and March 2025.
  • Focused on AI approaches utilizing neuroimaging, biomarkers, and cognitive assessments.
  • Deep learning models, particularly convolutional neural networks, achieved AUCs between 0.85 and 0.92.
  • Multimodal AI models showed higher predictive power than traditional methods.
  • Challenges included small sample sizes and limited external validation affecting generalizability.

Structured PICO

Do artificial intelligence models improve the prediction of mild cognitive impairment to Alzheimer's disease conversion compared to traditional methods?

P
Population
20 studies evaluating AI for predicting mild cognitive impairment (MCI) to Alzheimer's disease (AD) conversion involving neuroimaging, biomarkers, or multimodal frameworks
I
Intervention
Artificial intelligence (AI) models (including deep learning, convolutional neural networks, and multimodal frameworks)
C
Comparator
Traditional machine learning approaches
O
Outcome
Prediction of MCI-to-AD conversion (measured by accuracy, AUC, sensitivity, or specificity)surrogate

Deep learning and multimodal AI models demonstrate high accuracy (AUC 0.85-0.92) for predicting MCI-to-AD conversion, though external validation is needed for clinical integration.

Limitations

  • small sample sizes
  • lack of external validation
  • data standardization challenges
  • model bias

Cite This Study

Gupta et al. (2026) studied this question. Deep learning models outperformed traditional machine learning in predicting mild cognitive impairment to Alzheimer's disease conversion, achieving AUCs between 0.85 and 0.92.

synapsesocial.com/papers/69d895ea6c1944d70ce07225https://doi.org/10.4103/jmhhb.jmhhb_125_25
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1The Role of Artificial Intelligence in the Detection and Diagnosis of Neurocognitive Disorders: A Systematic Review2026
  2. 2Prediction Of Mild Cognitive Impairment to Alzheimer’s Disease Conversion Via Machine Learning2024
  3. 3Artificial Intelligence in the Automatic Diagnosis of Dementia Disorders: A Scoping Review of Techniques, Applications, and Challenges2026
  4. 4Harnessing Advanced AI Technologies to Enhance the Diagnosis of Alzheimer's Disease2026
  5. 5Predicting Alzheimer's Disease Using Artificial Intelligence and Machine Learning: A Comprehensive Analysis2024