{
  "docId": "019dd923-5e88-73ef-bd5c-fd5012384ae3",
  "docSlug": "bf350dd574c19997",
  "documentTitle": "2021 Air Street Capital The State of AI Report 2021",
  "authorId": "AirStreetCapital",
  "authorName": "Air Street Capital",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "vc_research",
  "sourceTypeLabel": "VC research",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.777,
  "pageNumber": 110,
  "pageCount": 188,
  "prevPage": 109,
  "nextPage": 111,
  "slideType": "case_study",
  "function": "illustrate_case",
  "density": "dense",
  "nDataPoints": 10,
  "notes": "The slide uses a PRISMA-style flow diagram to show the systematic review process of ML papers.",
  "elementsJson": [
    "bullet_list",
    "paragraph",
    "flowchart"
  ],
  "metadataConfidence": 0.95,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5e88-73ef-bd5c-fd5012384ae3/110",
  "deckHref": "/decks/019dd923-5e88-73ef-bd5c-fd5012384ae3",
  "deckJsonHref": "/decks/019dd923-5e88-73ef-bd5c-fd5012384ae3.json",
  "deckAnchorHref": "/decks/019dd923-5e88-73ef-bd5c-fd5012384ae3#slide-110",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "A systematic review of all papers published in 2020 that reported using ML for diagnosis and prognostication of Covid-19 found that \"none of the reviewed literature reaching the threshold of robustness and reproducibility essential to support utilization in clinical practice.\"",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd952-47c4-7719-a20b-70a1e8cb44d7",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.5,
        "w": 0.5,
        "x": 0.48,
        "y": 0.35
      },
      "kind": "diagram",
      "text": "PRISMA-style systematic review flow chart",
      "attrs": null,
      "subkind": "process",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "0c16c5f3-c7b4-424a-9c5d-90ad1c13140e",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.25,
        "w": 0.45,
        "x": 0.02,
        "y": 0.35
      },
      "kind": "list",
      "text": "A systematic review of all papers published in 2020 that reported using ML for diagnosis and prognostication of Covid-19 found that “none of the reviewed literature reaching the threshold of robustness and reproducibility essential to support utilization in clinical practice.” There were many methodological, dataset, and bias issues.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "4f18a4f8-6643-4580-8e5d-45e6adf9104b",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.1,
        "w": 0.45,
        "x": 0.02,
        "y": 0.65
      },
      "kind": "list",
      "text": "For example, 25% of papers used the same pneumonia control dataset to compare adult patients without mentioning that it consists of kids aged 1-5.",
      "attrs": null,
      "subkind": "bullet",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "a10b780d-4ad8-4f84-a394-acfcac1481fd",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.12,
        "w": 0.45,
        "x": 0.02,
        "y": 0.2
      },
      "kind": "paragraph",
      "text": "Despite a loud call to arms and many willing participants, the ML community has had surprisingly little positive impact against Covid-19. One of the most popular problems - diagnosing coronavirus pathology from chest X-ray or chest computed tomography images using computer vision - has been a universal clinical failure.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "475334c9-3daf-4819-a14f-5c7a52084b12",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.04,
        "w": 0.5,
        "x": 0.02,
        "y": 0.14
      },
      "kind": "title",
      "text": "Machine Learning in production: beware of bad data",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "1b160325-3112-4d6b-9108-f076939ae9b8",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [],
  "tools": [],
  "frameworks": [
    {
      "name": "PRISMA",
      "slug": null,
      "matchId": "b11e3fbe-c3c6-4ca1-838c-61fd856fd87f",
      "evidence": "Systematic review flow diagram",
      "confidence": 0.9
    }
  ],
  "arcBeats": [
    {
      "to": 152,
      "from": 5,
      "beatId": "019dd95a-0682-776c-8e35-0affbadec38e",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Facts (What)",
      "beatSlug": "triple-take-the-facts-what",
      "evidence": "Definitions p.5-6, Exec Summary p.7, then Sections 1-3 catalog evidence.",
      "position": 1,
      "confidence": 78,
      "parentBeatName": "Setup",
      "parentBeatSlug": "setup"
    },
    {
      "to": 152,
      "from": 10,
      "beatId": "019dd95a-0682-776c-8e35-1b3ee3a1c012",
      "arcName": "The Mountain",
      "arcSlug": "mountain",
      "beatName": "Rising Action",
      "beatSlug": "mountain-rising-action",
      "evidence": "Three sections accumulating evidence of accelerating progress.",
      "position": 2,
      "confidence": 55,
      "parentBeatName": "Development",
      "parentBeatSlug": "development"
    }
  ],
  "loops": [
    {
      "to": 111,
      "from": 106,
      "name": "Iceberg",
      "slug": "10-iceberg",
      "bestFor": "Consulting, complex problem solving, organizational change",
      "matchId": "019dd95a-07fe-70ce-8d3d-77cba6708c34",
      "evidence": "Data-centric pivot, benchmarking, distribution shifts, underspecification, bad data, data-driven AI.",
      "position": 14,
      "objective": "Diagnose ML-in-production challenges",
      "structure": "The Symptom (Visible) -> The System (Hidden) -> The Root Cause",
      "confidence": 78,
      "description": "Reveal that the visible problem is merely a symptom of a deeper root cause"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}