{
  "abstract": "Clinical AI assumes that the influence of training data can persist indefinitely. This premise fails when patients withdraw consent, evidence evolves, or bias is identified. Machine unlearning aims to remove specific data influence without full retraining. We argue that unlearning readiness should be built into the infrastructure of high-risk healthcare AI across patient autonomy, clinical validity, and system governance, and we outline a governance pathway to keep updates auditable and clinically safe.",
  "abstract_shown": true,
  "affiliations": [
    "Australian Institute for Machine Learning (AIML), The University of Adelaide, Adelaide, SA Australia",
    "Central Adelaide Local Health Network (CALHN), Adelaide, SA Australia",
    "South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA Australia",
    "Jones Radiology, Adelaide, SA Australia"
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Anthony Porter"
    },
    {
      "affs": [
        0
      ],
      "name": "Emily Kirkpatrick"
    },
    {
      "affs": [
        0
      ],
      "name": "Arpit Garg"
    },
    {
      "affs": [
        0
      ],
      "name": "Hemanth Saratchandran"
    },
    {
      "affs": [
        0
      ],
      "name": "Simon Lucey"
    },
    {
      "affs": [
        0,
        1,
        2,
        3
      ],
      "name": "Johan Verjans"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dgm56bwwny5rj.bib",
  "cited_by": 0,
  "doi": "10.1038/s41746-026-03050-1",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13748576/",
  "id": "dgm56bwwny5rj",
  "issns": [
    "2398-6352"
  ],
  "landing_url": "https://doi.org/10.1038/s41746-026-03050-1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Perspective"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7169672763",
  "pdf_url": "https://www.nature.com/articles/s41746-026-03050-1_reference.pdf",
  "pmcid": "PMC13748576",
  "pmid": "42471427",
  "published": "2026-07-18",
  "publisher": "Nature Publishing Group",
  "ris": "https://research.draconitic.com/paper/dgm56bwwny5rj.ris",
  "sources": [
    {
      "first_seen": "2026-11-13T12:26:33Z",
      "native_id": "PMC13748576",
      "source": "pmc",
      "url": "https://doi.org/10.1038/s41746-026-03050-1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Machine unlearning as a governance imperative for clinical AI",
  "title_text": "Machine unlearning as a governance imperative for clinical AI",
  "type": "peer-reviewed",
  "updated_at": "2026-11-14T12:21:32Z",
  "url": "https://research.draconitic.com/paper/dgm56bwwny5rj",
  "venue": "NPJ Digital Medicine",
  "versions": []
}