Examining the future of AI in Europe, a World Health Organization (WHO) working group has published a 40-page report on Europe’s AI readiness from a clinical perspective. EHTEL has selected two specific areas from the report’s five areas of strategic value. They are: clinical documentation and workflow and predictive analytics and risk assessment. Among its own range of insights, EHTEL includes here excerpts from its own working papers and a Briefing Paper.
What insights did the WHO SPI-DDH offer on implementation of AI?
The WHO is working in the context of a world in which it estimates that, by 2030, there will be a global shortfall of 11 million health workers by 2030. With this critical gap in mind, in 2025 the WHO European Region (WHO Europe) organised a series of working groups which focused on four key issues for global health. The series formed part of an initiative called the WHO Strategic Partners Initiative on Data and Digital Health (WHO SPI-DDH). One of the working groups (Working Group 1) concentrated on implementation insights into artificial intelligence (AI) in health care. A report has now been released on the group’s work on AI, entitled “Bridging theory and practice – implementation insights on artificial intelligence in health care”. It is particularly informative about what’s happening in WHO Member States’ health systems around the transformative potential of AI in health care and the practical realities of real-world AI deployment.
What is the WHO SPI-DDH report about?
Working Group 1 found that AI offers strategic value in health care across at least five fields. There are many of these five categories of AI-related activity that are fascinating, highly useful, and are already in wide use (among them are support for diagnostics and imaging).
These are:
- Operational efficiency and resource management: AI can streamline administrative tasks, resource allocation, and predictive capacity planning.
- Clinical documentation and workflow management: Solutions that leverage AI to automate routine clinical tasks – such as transcription, coding and scheduling – reduce clinician burden and improve care delivery efficiency.
- Personalised treatment and remote monitoring: AI applications can tailor health care to individual patient profiles and enable the continuous monitoring of health status outside of traditional care settings.
- Predictive analytics and risk assessment: Technologies use data-driven models to anticipate patient outcomes, identify high-risk individuals, and support early intervention strategies.
- Diagnostics and imaging: AI systems assist or automate the interpretation of medical images and diagnostic data to enhance accuracy, speed, and accessibility of diagnosis.
The report provided 11 detailed case studies from seven countries located across the whole of WHO Europe. The case studies described examples of what is happening in leading European Union (EU) Member States. They included experiences from Finland, Latvia, Portugal, and Spain – as well as from Israel, Norway, and the United Kingdom. The 11 show that real-world AI implementation is feasible. They illustrate both key outcomes and lessons learned:
Overall, the report concludes that the greatest barrier to successful AI adoption is no longer the availability of algorithms. Instead, it is the ability of health systems to implement AI tools safely, integrate them into clinical workflows, govern them appropriately, and continuously evaluate their real-world performance.
What other work is complementary to WHO Europe/WHO SPI DDH’s work?
The WHO SPI-DDH’s report provides a complementary read and set of findings to a more in-depth analysis by WHO Europe to the EU’s readiness to deploy AI in healthcare.
Compatible work is also provided by the EvalCommunity Academy. EvalCommunity Academy is an online community first set up in 2010. It is a global network that focuses on monitoring and evaluation. It offers practical certificated courses for professionals who want to save time; strengthen evidence work; improve reporting; and use AI with ethics, transparency, and human oversight.
EHTEL is pleased to see that the EvalCommunityAcademy has already analysed the WHO Europe’s work on AI reshaping health systems (Europe’s readiness for the use of AI). The academy has proposed a four-question test called an Interactive Monitoring and Evaluation Exercise. It enables monitoring and evaluation experts and people working in international (health) development to understand the main results of WHO Europe’s work on AI healthcare readiness.
What has EHTEL itself looked at in detail?
The five core categories of activity in which AI offers strategic value – emphasised by the WHO SPI-DDH – highlight AI’s transformative role in enhancing patient care, operational efficiency, and clinical work. These application areas range from short-term and easily deployable to much longer-term initiatives.
During late 2025 and early 2026, EHTEL did its own deep-dive into just two important areas of clinical work.
On clinical documentation and workflow management
Clinical documentation and workflow management were among the earliest types of AI to be deployed in healthcare.
EHTEL has outlined some key points on these topics in two of its 2026-published working papers and Briefing Paper. It did this through the EHTEL European Health Data Space (EHDS) Implementers’ Task Force, and in conjunction with projects like the Xt-EHR joint action and XiA.
Working Paper 2 is on the potential of AI for offering high-quality, structured health data. It explores workflows, especially in the context of health and care workforce, training, and culture.
Working Paper 3 is on clinical users making the best use of algorithm-based tools, including AI. It covers clinical decision support systems, as well as user-centred design with clinicians, and the need for the visibility/transparency of AI when working in clinical workflows.
EHTEL’s Briefing Paper, supported by the Xt-EHR project, is on the communication between EHDS-compliant EHR systems and algorithm-based tools. It emphasises the important of clinical workflows in several example use cases. The cases included congestive heart failure, Type 2 diabetes, and hip replacement. Example systems from Finland and Spain were cited. Annex 2 of the paper listed examples of AI systems which are already available on the market.
On predictive analytics and risk assessment
EHTEL is a member of currently-operating EU-supported projects which focus on AI developments related to predictive analytics and to risk assessment. These are very promising domains, but they require a good integration of quality data and an adapted infrastructure. Thus, they are only just beginning their implementation journey.
The entire focus of the COMFORTage project is on the prediction, monitoring, and personalised recommendations for the prevention and relief of dementia and frailty. EHTEL digital health facilitator, Luc Nicolas, and colleagues have argued why it is important to put prevention into people’s own hands and why technology can support that move. A COMFORTage policy brief focuses on standardisation of risk factors for dementia and frailty. It outlines the need for high-quality, harmonised data to ensure robust AI training. In COMFORTage’s learning centre, there is a knowledge oasis of resources about AI and other topics.
Not only does COMFORTage already work on virtual digital health twins, but so too will upcoming projects. The AIGENT project, for example, which is due to start in autumn 2026, will focus on how intelligent agents and digital twins can bridge different domains such as healthcare, housing and environment. At the same time, the agents and tools will use supportive AI infrastructure in an efficient and privacy preserving way.
What next for EHTEL?
EHTEL has long had an interest in capacity-building and in digital health-related training and education. It therefore welcomes the more global work of international organisations like EvalCommunityAcademy.
EHTEL anticipates that AI training (training on and in AI) will provide a hook for some of its own future activities.
More news on EHTEL’s work on AI training is coming up soon!
For more information
See the WHO Europe report on implementation insights on AI in health care report here.
See the WHO Europe report on AI reshaping health systems and European readiness for AI here.
See EHTEL’s insights into Europe’s AI readiness in health here.
See EHTEL’s own working papers and Briefing Paper – all around AI – here.
