{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T16:15:09Z","timestamp":1792512909041,"version":"4.5.0"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) is rapidly transforming how healthcare is delivered, but there is mounting evidence revealing a critical gap between technical sophistication and the human capacity to deploy, interpret, and govern these systems responsibly. Current ethical AI frameworks emphasize fairness, transparency, and accountability but they miss a foundational prerequisite: stakeholder AI literacy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This paper proposes a conceptual framework positioning digital health literacy, specifically AI-augmented health literacy, as the measurable literacy competencies among clinicians, patients, and governance professionals, even in ethically designed AI systems will fail to achieve trustworthy deployment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This paper is a critical narrative review with a normative governance purpose. This paper integrates evidence from three domains (1) documented AI failures in healthcare due to literacy gaps (2) systematic reviews of digital health literacy measurement tools revealing their inadequacy in the context of AI (3) insights from European data governance (General Data Protection Regulation [GDPR], European Health Data Space [EHDS] and AI Act) illustrating that legal harmonization without stakeholder comprehension produces fragmented, ineffective governance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The analysis of high-profile AI implementation failures suggest a recurring pattern in which literacy deficits at critical decision points may have been a contributing factor. Current measurement tools, including the eHealth Literacy Scale (eHEALS, developed 2006), lack AI-specific competencies and measure perceived instead of actual skills. We identify six AI literacy dimensions lacking in existing instruments, namely algorithmic literacy, bias awareness, data governance understanding, critical appraisal of AI outputs, trust calibration, and explainability interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These findings suggests that responsible AI in healthcare may require concurrent investment in human capacity building alongside technical and regulatory development. This paper proposes a three-tier literacy framework, encompassing clinician AI literacy, patient AI literacy, and governance AI literacy, with sector-specific competencies and assessment strategies. The three-tier framework proposed here is offered as a governance hypothesis requiring empirical validation rather than as a conclusion already established by the available evidence.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1832356","type":"journal-article","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T05:35:52Z","timestamp":1784612152000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["AI health literacy as a foundation for responsible AI in healthcare: a framework for trustworthy implementation"],"prefix":"10.3389","volume":"8","author":[{"given":"Olga","family":"Tzortzatou Nanopoulou","sequence":"first","affiliation":[{"name":"Digital Health Literacy and Policy Hub, Nanopoulos Foundation","place":["NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,21]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1038\/s41746-023-00852-5","article-title":"An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals","volume":"6","author":"Lambert","year":"2023","journal-title":"NPJ Digit Med"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1211150","DOI":"10.3389\/frhs.2023.1211150","article-title":"Implementing AI in healthcare the relevance of trust: a scoping review","volume":"3","author":"Steerling","year":"2023","journal-title":"Front Health Serv"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1001\/jamainternmed.2021.2626","article-title":"External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients","volume":"181","author":"Wong","year":"2021","journal-title":"JAMA Intern Med"},{"key":"B4","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Science"},{"key":"B5","author":"Ross","year":"2018"},{"key":"B6","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1007\/978-3-030-49388-2_22","article-title":"Biobanking across Europe post-GDPR: a deliberately fragmented landscape","volume-title":"GDPR and Biobanking: Individual Rights, Public Interest and Research Regulation Across Europe. 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