Skip to content
DraconiticResearch
Search Sources API
186,123 studies31 sourcesUpdated 53 min ago
Peer-reviewedCC BY-NC-ND 4.0Communications Medicine

The landscape of artificial intelligence in neurodegenerative diseases: a systematic review

  • Walter Endrizzi1,2
  • Flavio Ragni1
  • Stefano Bovo1
  • Avinash Chandra3
  • Monica Moroni1
  • Giuseppe Jurman1,4
  • Venet Osmani5
5 affiliations
  1. 1Fondazione Bruno Kessler, Data Science for Health, Trento, Italy
  2. 2University of Trento, Department of Cellular, Computational and Integrative Biology, Trento, Italy
  3. 3Centre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
  4. 4Department of Biomedical Sciences, Humanitas University, Milan, Italy
  5. 5Digital Environment Research Institute, Queen Mary University of London, London, UK
Read full textPDFDOICite

Abstract

Background: The rising global burden of neurodegenerative diseases underscores an urgent need for advanced research in diagnosis, prognosis, and treatment. Artificial Intelligence (AI) methods, particularly when applied to multimodal data, offer a powerful tool to address these challenges. However, a comprehensive overview and critique of the current landscape of AI methods is lacking.

Methods: 4,685 records of peer-reviewed, primary research articles were screened and 1,956 articles reviewed in full text, yielding 1,186 included studies. For each included study, clinical objectives, disease focus, data modalities, modelling approach, evaluation strategy, and reporting practices were extracted.

Results: Fewer than 5% of studies integrated pharmacological treatments into their predictive models, limiting the extent to which models can directly inform clinical decision-making. Neuroimaging was the predominant input modality, while integration of other clinically relevant data types was relatively rare. Reproducibility rates remain critically low at 35%, and external validation practices fail to use geographically and demographically diverse datasets.

Conclusions: Overall, AI research in neurodegenerative diseases suffers from significant limitations in reproducibility, data inclusivity, and clinical translatability. We provide a set of recommendations that can be adopted to address these issues and improve reliability and downstream clinical utility.

Related

  1. Innovative approaches: Utilizing artificial intelligence for early diagnosis of main neurodegenerative diseases.
    Hadi Ebrahimi, Ebrahim Hejazian, Zohre Eftekhari
    Caspian journal of internal medicinePeer-reviewedCC BY-NCFull textGeneral medicineEurope PMC
  2. Advances in artificial intelligence for brain imaging and computer-aided diagnosis of neurological diseases: a systematic literature review.
    Batyrkhan Omarov
    Frontiers in human neurosciencePeer-reviewedCC BYFull textEpidemiology & public healthEurope PMCPMC
  3. Biological age measured by DNA methylation clocks and neurodegenerative diseases: A systematic review and meta-analysis
    Z. Kua, J. H. Tay, A. Low +6
    medRxivPreprintAll rights reservedFull textNeurologymedRxiv
  4. Harnessing Artificial Intelligence and ChatGPT in Early Detection of Neurodegenerative Disorders: A Transformative Opportunity for Neurology
    Roberto Tedeschi
    Acta Neurologica TaiwanicaPeer-reviewedCC BY-NC-SAFull text2 citedEpidemiology & public healthDOAJ
  5. Digital and artificial intelligence-assisted medication management for older adults with chronic diseases: a systematic review and meta-analysis of randomized controlled trials
    Yuyan Cheng, Weidong Huang, Jianan Tian
    Frontiers in Public HealthPeer-reviewedCC BY 4.0Full textEpidemiology & public healthCrossref

Cite

BibTeXRIS
BibTeX
@article{endrizzi2026landscape,
  title = {{The landscape of artificial intelligence in neurodegenerative diseases: a systematic review}},
  author = {Walter Endrizzi and Flavio Ragni and Stefano Bovo and Avinash Chandra and Monica Moroni and Giuseppe Jurman and Venet Osmani},
  journal = {Communications Medicine},
  year = {2026},
  month = jul,
  doi = {10.1038/s43856-026-01669-5},
  url = {https://doi.org/10.1038/s43856-026-01669-5},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - The landscape of artificial intelligence in neurodegenerative diseases: a systematic review
AU  - Walter Endrizzi
AU  - Flavio Ragni
AU  - Stefano Bovo
AU  - Avinash Chandra
AU  - Monica Moroni
AU  - Giuseppe Jurman
AU  - Venet Osmani
PY  - 2026
DA  - 2026/07/03
JO  - Communications Medicine
DO  - 10.1038/s43856-026-01669-5
UR  - https://doi.org/10.1038/s43856-026-01669-5
AB  - Background: The rising global burden of neurodegenerative diseases underscores an urgent need for advanced research in diagnosis, prognosis, and treatment. Artificial Intelligence (AI) methods, particularly when applied to multimodal data, offer a powerful tool to address these challenges. However, a comprehensive overview and critique of the current landscape of AI methods is lacking.  Methods: 4,685 records of peer-reviewed, primary research articles were screened and 1,956 articles reviewed in full text, yielding 1,186 included studies. For each included study, clinical objectives, disease focus, data modalities, modelling approach, evaluation strategy, and reporting practices were extracted.  Results: Fewer than 5% of studies integrated pharmacological treatments into their predictive models, limiting the extent to which models can directly inform clinical decision-making. Neuroimaging was the predominant input modality, while integration of other clinically relevant data types was relatively rare. Reproducibility rates remain critically low at 35%, and external validation practices fail to use geographically and demographically diverse datasets.  Conclusions: Overall, AI research in neurodegenerative diseases suffers from significant limitations in reproducibility, data inclusivity, and clinical translatability. We provide a set of recommendations that can be adopted to address these issues and improve reliability and downstream clinical utility.
SN  - 2730-664X
ER  -