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  "story": {
    "story_id": "mp-2026-11-02-001",
    "source_story_id": "tmp-lead-gazerib-reader-panels",
    "edition_id": "mp-2026-11-02-morning-0115",
    "edition_url": "https://themachinepress.com/edition/2026-11-02",
    "position": 1,
    "story_type": "lead",
    "section": "research",
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    "headline": "Three Readers Capture Most of the Gaze Signal",
    "slug": "three-readers-capture-most-of-the-gaze-signal",
    "dek": "Every multi-reader panel improved chest X-ray severity prediction over the no-gaze baseline, while no individual reader reached significance on the main endpoint.",
    "summary": "Every multi-reader panel improved chest X-ray severity prediction over the no-gaze baseline, while no individual reader reached significance on the main endpoint.",
    "body_text": "GazeRIB asks whether radiologists' visual attention can supervise a model more reliably when it is pooled across readers. Eight experts repeatedly read the same 80 chest X-rays; the study compared individual readers with three-, five- and eight-reader panels across seven architectures and ten seeds, then evaluated 910 models on a separate 2,897-image cohort. Every panel improved macro F1 by 5.33 to 7.47 percentage points over the no-gaze baseline, while no individual reader reached significance on that endpoint. Three readers captured 73 to 80 percent of the eight-reader gain across macro F1, ROC-AUC and precision-recall area. External transfer was mixed: panels improved ROC-AUC on SIIM-FISABIO-RSNA, but not on MIMIC-Eye. The result supports panel composition as an experimental variable, not a claim that gaze guidance will transfer to every clinical setting.",
    "why_it_matters": "Every multi-reader panel improved chest X-ray severity prediction over the no-gaze baseline, while no individual reader reached significance on the main endpoint.",
    "limitations": [
      "External transfer was mixed: panels improved ROC-AUC on SIIM-FISABIO-RSNA, but not on MIMIC-Eye.",
      "The result supports panel composition as an experimental variable, not a claim that gaze guidance will transfer to every clinical setting."
    ],
    "importance": 10,
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    "first_published_at": "2026-11-02T09:00:00.000-05:00",
    "modified_at": "2026-11-02T09:00:00.000-05:00",
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        "text": "Every multi-reader panel improved chest X-ray severity prediction over the no-gaze baseline, while no individual reader reached significance on the main endpoint.",
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          "source-2026-11-02-001"
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        "qualification": "External transfer was mixed: panels improved ROC-AUC on SIIM-FISABIO-RSNA, but not on MIMIC-Eye."
      }
    ],
    "source_ids": [
      "source-2026-11-02-001"
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    "tags": [
      "medical imaging",
      "radiology",
      "gaze supervision",
      "evaluation"
    ],
    "image_url": "https://themachinepress.com/issues/2026-11-02/lead-radiologist-gaze.png",
    "corrections": []
  },
  "sources": [
    {
      "source_id": "source-2026-11-02-001",
      "title": "arXiv preprint 2610.42152, original v1",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2610.42152v1",
      "canonical_url": "https://arxiv.org/abs/2610.42152v1",
      "source_type": "primary_research",
      "is_primary_source": true,
      "published_at": "2026-10-30T13:59:59.000-04:00",
      "accessed_at": "2026-11-02T08:25:20.000-05:00",
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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": "Three Readers Capture Most of the Gaze Signal",
    "publisher": "The Machine Press",
    "published_at": "2026-11-02T09:00:00.000-05:00",
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