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    "story_id": "mp-2026-08-03-001",
    "source_story_id": "tmp-lead-medical-vlm-intersectional-fairness",
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    "headline": "The Missed-Diagnosis Gap Became a Training Signal",
    "slug": "the-missed-diagnosis-gap-became-a-training-signal",
    "dek": "A cross-modal alignment method narrowed intersectional performance gaps in skin-lesion and glaucoma classification while improving or preserving overall accuracy.",
    "summary": "A cross-modal alignment method narrowed intersectional performance gaps in skin-lesion and glaucoma classification while improving or preserving overall accuracy.",
    "body_text": "Researchers evaluated a training framework called CMAC-MMD on 10,015 HAM10000 skin-lesion images, an external set of 12,000 BCN20000 images and 10,000 fundus images. Compared with standard training, the reported dermatology true-positive-rate gap across intersecting age, gender and race groups fell from 0.50 to 0.26 while AUC rose from 0.94 to 0.97; the glaucoma gap fell from 0.41 to 0.31 while AUC moved from 0.71 to 0.72. The method does not require sensitive demographics at inference, but these are retrospective dataset results, not evidence of clinical deployment or the elimination of bias.",
    "why_it_matters": "A cross-modal alignment method narrowed intersectional performance gaps in skin-lesion and glaucoma classification while improving or preserving overall accuracy.",
    "limitations": [
      "The method does not require sensitive demographics at inference, but these are retrospective dataset results, not evidence of clinical deployment or the elimination of bias."
    ],
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    "first_published_at": "2026-08-03T09:00:00.000-04:00",
    "modified_at": "2026-08-03T09:00:00.000-04:00",
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        "text": "A cross-modal alignment method narrowed intersectional performance gaps in skin-lesion and glaucoma classification while improving or preserving overall accuracy.",
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        "qualification": "The method does not require sensitive demographics at inference, but these are retrospective dataset results, not evidence of clinical deployment or the elimination of bias."
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    "source_ids": [
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    "tags": [
      "medical AI",
      "fairness",
      "vision-language models",
      "diagnostics"
    ],
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    {
      "source_id": "source-2026-08-03-001",
      "title": "npj Digital Medicine: intersectional fairness in medical vision-language models",
      "publisher": "Springer Nature",
      "url": "https://doi.org/10.1038/s41746-026-03030-5",
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      "published_at": null,
      "accessed_at": "2026-08-03T08:27:38.249-04:00",
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  "publisher": {
    "name": "The Machine Press",
    "url": "https://themachinepress.com",
    "description": "A daily newspaper for the age of artificial intelligence."
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  "cite_this_report": {
    "title": "The Missed-Diagnosis Gap Became a Training Signal",
    "publisher": "The Machine Press",
    "published_at": "2026-08-03T09:00:00.000-04:00",
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