Evidence map›Paper›PMID 42269252›Full record

ArticleInternational journal of medical informatics2026

Artificial intelligence in health systems of the WHO European region: implementation, applications, opportunities, and barriers.

Keyrellous Adib, Hanna Emilia Dunning, Nicholas Letchford, Nagui Salama, Yiannos Tolias, Jerome De Barros, Natasha Azzopardi-Muscat, Hans Henri P Kluge, David Novillo-Ortiz

Abstract read
In one paragraph

Article in International journal of medical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Keyrellous AdibWHO Regional Office for Europe, UN City, Copenhagen, Denmark.
Hanna Emilia DunningWHO Regional Office for Europe, UN City, Copenhagen, Denmark.
Nicholas LetchfordWHO Regional Office for Europe, UN City, Copenhagen, Denmark.
Nagui SalamaWHO Regional Office for Europe, UN City, Copenhagen, Denmark.
Yiannos ToliasEuropean Commission, Brussels, Belgium.
Jerome De BarrosEuropean Commission, Brussels, Belgium.
Natasha Azzopardi-MuscatWHO Regional Office for Europe, UN City, Copenhagen, Denmark.
Hans Henri P KlugeWHO Regional Office for Europe, UN City, Copenhagen, Denmark.
David Novillo-OrtizWHO Regional Office for Europe, UN City, Copenhagen, Denmark. Electronic address: dnovillo@who.int.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is rapidly emerging as a transformative force in healthcare, with the potential to improve patient outcomes, enhance health system efficiency, and support data-driven decision-making. This study provides an overview of the current landscape, exploring how Member States are prioritizing AI in response to health system needs, engaging key stakeholders, and addressing critical enablers such as workforce upskilling and preparedness.

methodsThis study is based on a cross-sectional survey developed and administered by the WHO Regional Office for Europe, which was launched in June 2024 and remained open until March 2025. All responses were consolidated into a standardized database, reviewed for internal consistency, and analyzed using an exploratory, descriptive approach. Results are presented as percentages and absolute values, disaggregated at the regional, subregional, and EU27 levels.

findingsThe survey response rate was 94%, with 50 out of 53 Member States participating. The top opportunities rated by Member States for the use of AI in health were improving patient care and health outcomes (96%; 48/50), reducing pressure on the healthcare workforce (92%; 46/50), and enhancing health system efficiency (90%; 45/50). The most commonly reported applications of AI included AI-assisted diagnostics (64%; 32/50), conversational platforms or chatbots (50%; 25/50). The highest rated barriers rated to widespread adoption were legal uncertainty (48%; 24/50) and financial affordability (46%; 23/50). In terms of collaboration and engagement, 72% of Member States (36/50) reported stakeholder involvement, primarily through focus groups (46%; 23/50) and informal meetings, seminars, or workshops (44%; 22/50). Of the 36 Member States reporting at least one form of engagement, the stakeholders most frequently engaged were government actors (81%; 29/36), healthcare providers (75%; 27/36), and AI developers (75%; 27/36), whereas patient associations (42%; 15/36) and the general public (22%; 8/36) were less commonly involved. Finally, with regard to health workforce education and AI literacy, the study highlights a significant gap in preparedness, where only 24% of Member States (12/50) reported offering in-service AI training for the health workforce, and 20% (10/50) offered pre-service training.

interpretationThe integration of AI in healthcare across the WHO European Region is advancing rapidly, demonstrating potential to enhance patient outcomes and alleviating workforce pressures. However, its widespread and equitable adoption remains limited by challenges related to affordability, regulatory and legal ambiguities, insufficient patient engagement, and the need for strategic investment in workforce training and upskilling.

Indexed as

Artificial IntelligenceDelivery of Health CareCross-Sectional StudiesDigital HealthEuropeHumansSurveys and QuestionnairesWorld Health OrganizationAI literacyArtificial intelligenceHealth systemsHealth workforce capacity

Identifiers

PMID42269252
PMCPMC13343123

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.