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 "identifier": "kaal:position:2026-07-31-7280",
 "additionalType": "https://wulfkaal.github.io/positions/schema.json#AffirmedPositionClaim",
 "name": "Streaming Human Centric Explainable Ai In Healthcare A Review Of Methods Evaluatio D6F80943Bf",
 "text": "Human-Centric Explainable AI in Healthcare: A Review of Methods, Evaluation Metrics, and Ethical Governance (Preprint) presents the following source proposition: However, opaque “black-box” models pose challenges for clinician trust, safety, accountability, and regulatory compliance. This proposition is pertinent to Kaal's source-bound claim that 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. The proposed response is an extension: the relationship should remain limited to the retrieved source proposition and the mapped Kaal claim unless fuller source review supports a broader conclusion.",
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
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 "datePublished": "2026-07-31",
 "dateModified": "2026-07-31",
 "creativeWorkStatus": "Affirmed",
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  "ai-and-agents",
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  "scholarly-literature",
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  "The response is limited to the retrieved source proposition and mapped Kaal claim unless fuller source review supports a broader conclusion.",
  "External evidence level: abstract indexed.",
  "Mapping review tier: moderate-confidence claim review.",
  "Primary mapping confidence: 0.4493.",
  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
 ],
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  "name": "Human-Centric Explainable AI in Healthcare: A Review of Methods, Evaluation Metrics, and Ethical Governance (Preprint)",
  "url": "https://doi.org/10.2196/preprints.89088"
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  "identifier": "kaal:claim:4855607-024",
  "url": "https://wulfkaal.github.io/claims/4855607-024",
  "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
  "paper": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance",
  "authors": [
   "Wulf A. Kaal"
  ],
  "year": "2024",
  "ssrn": "https://ssrn.com/abstract=4855607",
  "source_pdf_sha256": "eb0b3e62374b45a8fa888c6bde9725e606bcb46cf4b5e74a6e851d9f25099113"
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   "@type": "CreativeWork",
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   "url": "https://doi.org/10.2196/preprints.89088"
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 "publicationStatus": "public",
 "recordTypeNote": "Dated commentary position extending a scholarly corpus claim. Not a verbatim claim extracted from the paper.",
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 "mappingAmbiguous": true,
 "mappingMethod": "idf-weighted multi-field mapping v1",
 "mappingWhyRelevant": "Shared high-information concepts: healthcare, ethical, governance, safety, regulatory, compliance. Scope: sectors where AI applications must rapidly adapt to new laws and ethical considerations.",
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  "workAuthors": [
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    "ambiguous": true
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    "whyRelevant": "Shared high-information concepts: human, centric, trust, compliance. Scope: applies to trust generated by legal guarantees and judicial enforcement.",
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    "whyRelevant": "Shared high-information concepts: opaque, trust, compliance. Scope: decentralized networks without a central approving authority.",
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    "whyRelevant": "Shared high-information concepts: human, review, governance, compliance. Scope: applies to code reviews audited by the CRDAO community.",
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