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 "identifier": "kaal:entity:healthcare-ai",
 "name": "Healthcare ai",
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 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0000-0003-0757-275X"
 },
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   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4855607-024",
   "identifier": "kaal:claim:4855607-024",
   "text": "WDAGs allow new regulatory and ethical standards to be integrated into existing AI systems without overhauling the entire model architecture, which is what makes rapid legal adaptation feasible in sectors such as public safety and healthcare.",
   "abstract": "In the case of federal AI learning models, for instance, WDAGs facilitate the integration of new regulatory and ethical standards into existing AI systems without the need to overhaul the entire model architecture.",
   "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
   "datePublished": "2024",
   "claim_type": "mechanism",
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   "scope_conditions": [
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   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/5095633-015",
   "identifier": "kaal:claim:5095633-015",
   "text": "In healthcare, biased or stale training data produces algorithms that misdiagnose underrepresented populations and thereby reinforce existing health disparities instead of reducing them.",
   "abstract": "In healthcare, for instance, biased or stale training data could lead to algorithms that misdiagnose underrepresented populations, reinforcing existing health disparities rather than alleviating them.",
   "citation": "Wulf A. Kaal, Artificial Intelligence The Final Frontier (2025). SSRN: https://ssrn.com/abstract=5095633",
   "datePublished": "2025",
   "claim_type": "failure",
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}