AI in health as a governance challenge: findings from WHO Europe’s knowledge community
Abstract
Responsible AI governance in health is a global policy priority, yet frameworks remain largely normative and disconnected from practitioner experience. WHO Europe’s Knowledge Community (KC) illustrates how WHO’s convening power can consult a diverse, cross-continental range of health professionals to translate their common viewpoints into actionable governance evidence. Between October and December 2025, 898 professionals registered across all continents on a virtual asynchronous platform; 80 contributed substantively to five weekly discussions covering definitions of ethical AI, real-world examples, barriers and enablers, urgency prioritization, and priority actions. AI-assisted thematic and content analysis, anchored in WHO’s five governance pillars, was applied to all discussion transcripts. A Slido poll (77 participants) captured non-negotiable deployment values.
Findings indicate that responsible AI in health is primarily a planning and governance challenge rather than a technical one. Human autonomy and agency (51%) and human well-being and safety (49%) were identified as non-negotiable values. Practitioners converged on governance capacity and accountability as the most urgent concern. Key barriers included governance gaps, unclear accountability, fragmented datasets, data privacy concerns, and AI literacy deficits. Key enablers were enforceable governance, human oversight, co-design with clinicians and patients, and institutional ethics frameworks. Priority actions included regulatory harmonization, representative data standards, and AI literacy frameworks. These findings affirm the KC as an effective method for capturing diverse, lay practitioner perspectives on complex governance challenges. Responsible AI adoption requires enforceable governance frameworks before deployment, and WHO’s convening power can systematically generate the practitioner evidence base needed to build them.
Key messages: • Responsible AI in health is primarily a planning and governance challenge, not a technical one; practitioners globally identify governance capacity and accountability as the most urgent priority.
• WHO Knowledge Communities can harness WHO’s convening power to gather diverse global practitioner viewpoints and translate them into evidence for governance policy.
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BibTeX
@article{sy2026health,
title = {{AI in health as a governance challenge: findings from WHO Europe’s knowledge community}},
author = {TRL Sy and SO Skrøvseth and D Novillo Ortiz and N Azzopardi Muscat},
journal = {The European Journal of Public Health},
year = {2026},
month = oct,
doi = {10.1093/eurpub/ckag169.1222},
url = {https://doi.org/10.1093/eurpub/ckag169.1222},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - AI in health as a governance challenge: findings from WHO Europe’s knowledge community
AU - TRL Sy
AU - SO Skrøvseth
AU - D Novillo Ortiz
AU - N Azzopardi Muscat
PY - 2026
DA - 2026/10/01
JO - The European Journal of Public Health
DO - 10.1093/eurpub/ckag169.1222
UR - https://doi.org/10.1093/eurpub/ckag169.1222
AB - Responsible AI governance in health is a global policy priority, yet frameworks remain largely normative and disconnected from practitioner experience. WHO Europe’s Knowledge Community (KC) illustrates how WHO’s convening power can consult a diverse, cross-continental range of health professionals to translate their common viewpoints into actionable governance evidence. Between October and December 2025, 898 professionals registered across all continents on a virtual asynchronous platform; 80 contributed substantively to five weekly discussions covering definitions of ethical AI, real-world examples, barriers and enablers, urgency prioritization, and priority actions. AI-assisted thematic and content analysis, anchored in WHO’s five governance pillars, was applied to all discussion transcripts. A Slido poll (77 participants) captured non-negotiable deployment values. Findings indicate that responsible AI in health is primarily a planning and governance challenge rather than a technical one. Human autonomy and agency (51%) and human well-being and safety (49%) were identified as non-negotiable values. Practitioners converged on governance capacity and accountability as the most urgent concern. Key barriers included governance gaps, unclear accountability, fragmented datasets, data privacy concerns, and AI literacy deficits. Key enablers were enforceable governance, human oversight, co-design with clinicians and patients, and institutional ethics frameworks. Priority actions included regulatory harmonization, representative data standards, and AI literacy frameworks. These findings affirm the KC as an effective method for capturing diverse, lay practitioner perspectives on complex governance challenges. Responsible AI adoption requires enforceable governance frameworks before deployment, and WHO’s convening power can systematically generate the practitioner evidence base needed to build them. Key messages: • Responsible AI in health is primarily a planning and governance challenge, not a technical one; practitioners globally identify governance capacity and accountability as the most urgent priority. • WHO Knowledge Communities can harness WHO’s convening power to gather diverse global practitioner viewpoints and translate them into evidence for governance policy.
SN - 1101-1262
SN - 1464-360X
ER -