{
  "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.\n\nMethods: 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.\n\nResults: 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.\n\nConclusions: 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.",
  "abstract_shown": true,
  "affiliations": [
    "Fondazione Bruno Kessler, Data Science for Health, Trento, Italy",
    "University of Trento, Department of Cellular, Computational and Integrative Biology, Trento, Italy",
    "Centre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University of London, London, UK",
    "Department of Biomedical Sciences, Humanitas University, Milan, Italy",
    "Digital Environment Research Institute, Queen Mary University of London, London, UK"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Walter Endrizzi",
      "orcid": "0009-0001-3335-2235"
    },
    {
      "affs": [
        0
      ],
      "name": "Flavio Ragni",
      "orcid": "0000-0001-7767-7288"
    },
    {
      "affs": [
        0
      ],
      "name": "Stefano Bovo",
      "orcid": "0000-0002-0278-6786"
    },
    {
      "affs": [
        2
      ],
      "name": "Avinash Chandra",
      "orcid": "0009-0005-5072-7499"
    },
    {
      "affs": [
        0
      ],
      "name": "Monica Moroni",
      "orcid": "0000-0003-1852-7217"
    },
    {
      "affs": [
        0,
        3
      ],
      "name": "Giuseppe Jurman",
      "orcid": "0000-0002-2705-5728"
    },
    {
      "affs": [
        4
      ],
      "name": "Venet Osmani",
      "orcid": "0000-0001-7306-2972"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/900rtbnqqvvnt.bib",
  "cited_by": 0,
  "doi": "10.1038/s43856-026-01669-5",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13696689/",
  "id": "900rtbnqqvvnt",
  "issns": [
    "2730-664X"
  ],
  "landing_url": "https://doi.org/10.1038/s43856-026-01669-5",
  "language": "en",
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7167276603",
  "pdf_url": "https://www.nature.com/articles/s43856-026-01669-5_reference.pdf",
  "pmcid": "PMC13696689",
  "pmid": "42399403",
  "published": "2026-07-03",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/900rtbnqqvvnt.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:00Z",
      "native_id": "PMC13696689",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s43856-026-01669-5"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "The landscape of artificial intelligence in neurodegenerative diseases: a systematic review",
  "title_text": "The landscape of artificial intelligence in neurodegenerative diseases: a systematic review",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:33:59Z",
  "url": "https://research.draconitic.com/paper/900rtbnqqvvnt",
  "venue": "Communications Medicine",
  "versions": []
}