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Peer-reviewedCC BY 4.0Frontiers in Neurology

Advances in artificial intelligence-driven MRI research in Alzheimer’s disease

  • Lihua Deng1,2,3
  • Yan Zeng4
  • Peiling Ou3,5
  • Suyi Zhou3
  • Yu Gou2
  • Cong Huang1,2,6
6 affiliations
  1. 1Department of Radiology, The First People’s Hospital of Neijiang
  2. 2Department of Radiology, No. 926 Hospital, Joint Logistics Support Force of PLA
  3. 37T Magnetic Resonance Imaging Translational Medical Center, Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University)
  4. 4Department of Ultrasound, The First People’s Hospital of Neijiang
  5. 5Department of Radiology, Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College
  6. 6Department of Nuclear Medicine, The First People’s Hospital of Neijiang
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Abstract

Alzheimer’s disease (AD), the most prevalent neurodegenerative disorder, poses substantial challenges for early diagnosis and longitudinal monitoring—two cornerstones of effective clinical management and therapeutic intervention. Magnetic resonance imaging (MRI), owing to its non-invasiveness and high spatial resolution, has emerged as an indispensable tool in AD research. In recent years, artificial intelligence (AI), particularly deep learning (DL) and machine learning (ML) techniques, has demonstrated remarkable potential for automating MRI analysis, extracting subtle imaging features, and supporting objective disease prediction. This review comprehensively summarizes the latest advances in AI-driven MRI applications for AD diagnosis, staging, prognosis, and adjuvant therapeutic monitoring. It highlights key progress in AI model development, multimodal data processing, and clinical translational value. Furthermore, current bottlenecks, including data heterogeneity, model interpretability, and real-world deployment, are systematically discussed, and future directions are outlined to enhance the precision and clinical utility of AI-assisted MRI in AD. Overall, AI-MRI has evolved from laboratory validation toward early clinical adoption, with explainability and multi-center generalizability identified as critical priorities in the next research phase.

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Cite

BibTeXRIS
BibTeX
@article{deng2026advances,
  title = {{Advances in artificial intelligence-driven MRI research in Alzheimer’s disease}},
  author = {Lihua Deng and Yan Zeng and Peiling Ou and Suyi Zhou and Yu Gou and Cong Huang},
  journal = {Frontiers in Neurology},
  year = {2026},
  month = oct,
  doi = {10.3389/fneur.2026.1943265},
  url = {https://www.frontiersin.org/articles/10.3389/fneur.2026.1943265/full},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Advances in artificial intelligence-driven MRI research in Alzheimer’s disease
AU  - Lihua Deng
AU  - Yan Zeng
AU  - Peiling Ou
AU  - Suyi Zhou
AU  - Yu Gou
AU  - Cong Huang
PY  - 2026
DA  - 2026/10/08
JO  - Frontiers in Neurology
DO  - 10.3389/fneur.2026.1943265
UR  - https://www.frontiersin.org/articles/10.3389/fneur.2026.1943265/full
AB  - Alzheimer’s disease (AD), the most prevalent neurodegenerative disorder, poses substantial challenges for early diagnosis and longitudinal monitoring—two cornerstones of effective clinical management and therapeutic intervention. Magnetic resonance imaging (MRI), owing to its non-invasiveness and high spatial resolution, has emerged as an indispensable tool in AD research. In recent years, artificial intelligence (AI), particularly deep learning (DL) and machine learning (ML) techniques, has demonstrated remarkable potential for automating MRI analysis, extracting subtle imaging features, and supporting objective disease prediction. This review comprehensively summarizes the latest advances in AI-driven MRI applications for AD diagnosis, staging, prognosis, and adjuvant therapeutic monitoring. It highlights key progress in AI model development, multimodal data processing, and clinical translational value. Furthermore, current bottlenecks, including data heterogeneity, model interpretability, and real-world deployment, are systematically discussed, and future directions are outlined to enhance the precision and clinical utility of AI-assisted MRI in AD. Overall, AI-MRI has evolved from laboratory validation toward early clinical adoption, with explainability and multi-center generalizability identified as critical priorities in the next research phase.
SN  - 1664-2295
ER  -