Artificial intelligence is no longer theoretical within European healthcare systems. It underpins imaging diagnostics, informs predictive analytics, streamlines clinical data workflows, and supports strategic health planning.
However, the WHO’s 2025 report, Artificial Intelligence Is Reshaping Health Systems: State of Readiness Across the WHO European Region—drawing on a 2024–2025 survey of 50 out of 53 countries—paints a less celebratory picture than the technology narrative often conveys. While adoption is advancing, regulatory coherence and institutional readiness remain uneven across the region.
National Strategies between Ambition and Generality
The most emblematic finding concerns national strategies.
Only 8% of countries have published an AI strategy specifically dedicated to healthcare. By contrast, 66% have adopted cross-sector strategies that include health among their areas of application. In other words, AI is largely embedded within broad digital agendas—without a health-specific regulatory and operational framework.
For the pharmaceutical value chain, this distinction is far from marginal. In the absence of a clearly defined, health-specific architecture, the risk is regulatory fragmentation, with divergent interpretations around model validation, liability, post-market monitoring, and transparency or explainability requirements.
In Southern Europe—including Italy—38% of countries report having no dedicated health AI strategy at all, further amplifying regional asymmetries.
The real bottleneck: Legal accountability
If AI is firmly on Europe’s healthcare agenda, legal liability is not.
Only 8% of countries have developed clear accountability standards governing the use of artificial intelligence in healthcare. Even more striking, just 6% have introduced specific legal requirements for generative AI systems applied in clinical settings.
Regulatory uncertainty is identified by 86% of member states as the primary barrier to adoption, followed by financial constraints (78%). The core issue is not access to technology. It is the absence of clear rules defining responsibility when an algorithm generates an error, produces clinically relevant bias, or delivers an output that has not been adequately validated.
For pharmaceutical companies and digital health solution providers, this translates into regulatory and reputational exposure within a framework that is still consolidating. The EU AI Act (Regulation 2024/1689), the European Health Data Space (EHDS), and national provisions are evolving in parallel, yet operational alignment remains incomplete.
In a sector built on traceability and auditability—GMP, GCP, and pharmacovigilance standards—ambiguity around liability slows partnerships, delays validation of AI models for real-world evidence, and complicates integration into regulatory decision-making processes.
Data Governance: the fragile foundation of Healthcare AI
The report devotes significant attention to data governance maturity. While 66% of countries have adopted a national health data strategy, only 30% have issued clear guidance on the secondary use of data for public research and cross-border data sharing.
For the pharmaceutical sector, this is not a peripheral issue—it is structural. AI applications in clinical research, pharmacovigilance, drug repurposing, and real-world evidence depend on interoperable and standardized data ecosystems. Without meaningful harmonization—particularly in light of the forthcoming European Health Data Space (EHDS)—model scalability remains confined to national silos, constraining Europe’s overall competitive position.
The emerging risk is a multi-speed digital Europe: some countries evolve into experimentation hubs capable of attracting investment and advanced research partnerships, while others remain peripheral observers.
From strategy to funding: the Operational Gap
A further critical issue lies in the transition from planning to investment. Only slightly more than half of the countries that have identified national priorities for AI in healthcare have actually allocated dedicated funding.
The gap between declared strategy and operational financing raises a fundamental industrial concern. Without sustained capital commitments, AI initiatives risk remaining confined to pilot projects, unable to evolve into systemic infrastructure.
For the pharmaceutical value chain—accustomed to operating in highly regulated and capital-intensive environments—regulatory predictability and stable public investment frameworks are essential. They underpin public–private partnerships, enable the integration of AI tools into regulatory pathways, and support the development of sustainable innovation models.
Explainability, competenze e responsabilità organizzativa
Ninety-two percent of surveyed countries identify clear accountability rules as a prerequisite for the widespread adoption of AI. Ninety percent emphasize the importance of transparency, verifiability, and explainability.
In the pharmaceutical sector—built on scientific validation and traceability—artificial intelligence cannot operate as a black box. Industrial deployment requires auditable models, structured documentation, and lifecycle monitoring, from training and deployment to post-market surveillance.
Yet there is an additional layer, often underestimated: internal capability. The WHO report highlights that only a minority of countries have integrated AI into formal healthcare education pathways, and fewer than half have established dedicated specialist roles.
With the entry into force of the European AI Act, organizations that develop or deploy AI systems are required to ensure an adequate level of AI literacy among their personnel. This is not a procedural formality—it is an organizational responsibility. Without widespread competence in risk assessment, regulatory requirements, technical limitations, and ethical implications, governance frameworks remain largely theoretical.
For companies across the pharmaceutical value chain, this translates into structured investment in training programs that integrate regulatory, technical, and quality assurance dimensions. In this context, specialized initiatives such as AI in Control—focused on the compliant management of AI within pharma quality, validation, and governance processes—illustrate how compliance can evolve from constraint to strategic lever.
Key Figures from the WHO Europe 2025 Report
- Health-specific AI strategy: 8%
- Cross-sector AI strategy (including health): 66%
- Primary barrier: legal uncertainty: 86%
- Financial constraints cited as barrier: 78%
- Need for clear liability rules: 92%
- Need for transparency and explainability: 90%
- Clear guidelines for secondary data use: 30%
- Pre-service AI training in healthcare education: 20%
European Healthcare AI between ambition and realism
The WHO report does not portray an unprepared continent. It depicts an ecosystem in transition. Strategies are in place, awareness is widespread, and industrial interest is growing. Yet regulatory maturity remains uneven, and legal accountability frameworks are still incomplete.
For the European pharmaceutical industry, the question is no longer whether artificial intelligence will become embedded in regulatory, clinical, and manufacturing processes. The question is at what speed—and under what safeguards.
Those able to integrate technological innovation with rigorous compliance and sustained capability development will build a durable competitive advantage. Those waiting for full regulatory harmonization may find themselves in a reactive position.
Is Europe ready for AI in healthcare? The WHO data suggest that the direction is clear. But the robustness of the journey will depend on the ability to convert strategies into coherent, interoperable, and sustainable rules—and to transform skills into embedded organizational culture.
