{
  "abstract": "Artificial intelligence (AI) in global health is often framed through a narrative of technological salvation, in which models and systems developed in high-income countries (HICs) are exported to low and middle-income countries (LMICs) as turnkey solutions. This top-down paradigm overlooks local expertise and frequently produces context-mismatched, unsustainable tools. We argue for a shift towards cocreation and reverse knowledge flow, in which LMIC innovators lead AI initiatives across the entire pipeline, from problem definition and data stewardship to model development, validation, deployment and governance. Rather than ‘contextualising’ HIC-built systems, cocreated AI is rooted in local health system realities, leveraging local providers and community health workers, cost-conscious innovation and decentralised care models.\n\nEarly examples from LMICs suggest that locally led AI decision-support platforms can improve access, quality and continuity of care. Building on emerging South–South collaborations, we describe how networks of LMIC-based AI researchers can generate solutions validated across diverse populations and inform new global standards.\n\nTo enable this, international funding, evaluation and governance architectures must be restructured to require LMIC coleadership, data sovereignty, equitable intellectual property arrangements and long-term capacity building. Grounding global AI norms in ethical frameworks originating in LMICs, such as African discourses on data sovereignty and Ubuntu ethics, can prevent the universalisation of HIC-centric values. Repositioning LMICs as engines of innovation is a practical necessity to realise equitable, effective AI for global health.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas, USA",
    "Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA",
    "ICanServe Foundation, Manila, Philippines",
    "Department of Radiation Oncology, University of Santo Tomas, Manila, Philippines"
  ],
  "article_type": "discussion",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Milit S Patel",
      "orcid": "0009-0000-9179-267X"
    },
    {
      "affs": [
        2
      ],
      "name": "Maria Jocelyn Kara Magsanoc-Alikpala"
    },
    {
      "affs": [
        3
      ],
      "name": "Luisa E Jacomina"
    },
    {
      "affs": [
        1
      ],
      "name": "Luke R G Pike"
    },
    {
      "affs": [
        1
      ],
      "name": "Puneeth Iyengar"
    },
    {
      "affs": [
        1
      ],
      "name": "Nancy Y Lee"
    },
    {
      "affs": [
        1
      ],
      "name": "Edward Christopher Dee",
      "orcid": "0000-0001-6119-0889"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bht55qphca7mz.bib",
  "cited_by": 0,
  "doi": "10.1136/bmjhci-2026-102172",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13723796/",
  "id": "bht55qphca7mz",
  "issns": [
    "2632-1009"
  ],
  "landing_url": "https://doi.org/10.1136/bmjhci-2026-102172",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "1506"
    ],
    [
      "heading",
      "Perspective"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7224084077",
  "pdf_url": "https://informatics.bmj.com/content/33/1/e102172.full.pdf",
  "pmcid": "PMC13723796",
  "pmid": "42951326",
  "published": "2026-10-28",
  "publisher": "BMJ Publishing Group",
  "ris": "https://research.draconitic.com/paper/bht55qphca7mz.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T12:22:22Z",
      "native_id": "PMC13723796",
      "source": "pmc",
      "url": "https://doi.org/10.1136/bmjhci-2026-102172"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Flipping the script: co-creation and local innovation in AI for global health paradigms",
  "title_text": "Flipping the script: co-creation and local innovation in AI for global health paradigms",
  "type": "peer-reviewed",
  "updated_at": "2026-11-04T12:22:22Z",
  "url": "https://research.draconitic.com/paper/bht55qphca7mz",
  "venue": "BMJ Health & Care Informatics",
  "versions": []
}