{
  "title": "AI’s Healing Powers",
  "sourceUrl": "https://www.imf.org/en/publications/fandd/issues/2023/12/case-studies-ai-healing-powers-kerry-dooley-young",
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  "summary": "Artificial intelligence shows promise in medicine, but there are recognized drawbacks and risks",
  "sections": [
    {
      "heading": "Summary and context",
      "content": "- Article title: AI’s Healing Powers By KERRY DOOLEY YOUNG\n- Publication: F&D Magazine\n- Date: December 2023\n- Piece type: Case Study / short overview\n- Central claim: Artificial intelligence shows promise in medicine, but there are recognized drawbacks and risks."
    },
    {
      "heading": "Demonstrated applications and empirical findings",
      "content": "- LumineticsCore system (Digital Diagnostics) used in a study in Rangpur, Bangladesh:\n  - The system uses a special kind of camera designed to capture images of the eyes and evaluates them with AI.\n  - Historical regulatory and payer milestones for the product:\n    - 2018: the first AI-driven device to gain US Food and Drug Administration (FDA) clearance to check for diabetic retinopathy.\n    - 2020: US Medicare agreed to pay for use of the device in primary care offices.\n  - Trial productivity result (reported by Michael Abramoff et al. in Nature’s npj Digital Medicine, October):\n    - When the AI tool was used, an estimated 1.59 patients an hour received what was deemed a high-quality visit, versus 1.14 in the control group.\n  - Conclusion drawn by study proponents: LumineticsCore could help more people get screened for eye damage from diabetes, even in developing economies.\n\n- AI in pharmaceutical research:\n  - AI helps understand in early stages how compounds will work in the body, reducing the chance of side effects in later testing.\n  - AI enables rapid synthesis of information about experimental drugs that would previously have taken years.\n  - Quoted implication: “The efficiencies that have been built now on the discovery side are exponential,” and “We’re going to see a lot coming to the market soon.”"
    },
    {
      "heading": "Risks, biases, and equity concerns",
      "content": "- Documented harm from biased algorithms:\n  - 2019 Science paper found an algorithm widely used by large health systems and insurers underestimated the severity of Black patients’ illness, thereby risking denial of care.\n- Source-of-data bias concerns:\n  - Worries about developing AI tools based on data drawn with a tilt toward relatively wealthy people, who are often White and have good access to health care.\n- Diversity needs:\n  - Greater diversity is essential among patients whose data train AI tools and among the people who build these products.\n  - Quotation: “You’re going to need to have multiracial, multicultural coders,” said Jerome Singh.\n- Global South deployment challenges:\n  - Comparative doctor density (WHO data cited in the article):\n    - United States: about 36 doctors for every 10,000 people\n    - United Kingdom: about 32 doctors for every 10,000 people\n    - India: about 7 per 10,000 people\n  - Infrastructure and institutional barriers cited: lack of electricity, lack of computer servers, shortage of workers able to translate AI-assisted diagnoses into effective treatment.\n  - Framing of adoption difficulty: “In some settings, it’s going to be more of a sprint. In other settings, it’s going to be a marathon.”"
    },
    {
      "heading": "Governance, regulation, and recommended safeguards",
      "content": "- Regulatory activity and guidance referenced:\n  - 2021 WHO guidance report on ethics and governance of AI for health (advisors include Jerome Singh and Partha Majumder).\n  - October (year not restated in text for WHO’s new report): WHO issued a new report outlining challenges of regulating AI in medicine, citing concerns about rapid deployment of tools derived from large language models without full understanding of benefits/harms.\n  - A European Parliament report (issued last year in the article’s timeline) noted concerns about lack of transparency and privacy and security issues.\n  - The FDA is working to refine its approach to regulating AI in medical products through formal guidance, specifying what evidence companies will need for FDA clearance.\n- Policy and professional recommendations quoted or paraphrased:\n  - Acceptance of AI adoption in medicine as inevitable: “We have to accept that this is reality,” said Partha Majumder.\n  - Need for checks and balances to prevent inappropriate predictions and diagnoses.\n  - Emphasis on building regulatory frameworks that define evidentiary standards for safety and effectiveness."
    },
    {
      "heading": "Framing and distinctions",
      "content": "- Terminology distinction offered by Michael Abramoff:\n  - “Impact AI” — AI shown to help improve people’s health.\n  - “Glamor AI” — products that garner tantalizing headlines but do not yet show hard evidence of benefit to patients.\n  - Policy implication: Favor and invest in “impact AI,” and avoid paying too much for technology that does not improve outcomes.\n\n- Ethical and practical imperative:\n  - Vigilance required because AI can both help and harm patients.\n  - Calls for more diverse training data, diverse developer teams, and careful regulatory oversight.\n\nAI’s Healing Powers — KERRY DOOLEY YOUNG, F&D Magazine, December 2023\n\n---\n\n\nSource: https://www.imf.org/en/publications/fandd/issues/2023/12/case-studies-ai-healing-powers-kerry-dooley-young"
    }
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    "Authors: KERRY DOOLEY YOUNG",
    "Published: December 1, 2023",
    "Article title: AI’s Healing Powers By KERRY DOOLEY YOUNG",
    "Publication: F&D Magazine",
    "Date: December 2023",
    "Piece type: Case Study / short overview",
    "Central claim: Artificial intelligence shows promise in medicine, but there are recognized drawbacks and risks.",
    "LumineticsCore system (Digital Diagnostics) used in a study in Rangpur, Bangladesh:",
    "AI in pharmaceutical research:",
    "Documented harm from biased algorithms:",
    "Source-of-data bias concerns:",
    "Diversity needs:",
    "Global South deployment challenges:",
    "Regulatory activity and guidance referenced:",
    "Policy and professional recommendations quoted or paraphrased:",
    "Terminology distinction offered by Michael Abramoff:",
    "Ethical and practical imperative:"
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