Microsoft is one of the world’s leading technology suppliers with solutions that reach widely across different industries, including healthcare. Microsoft has been involved in the CleverHealth Network ecosystem, led by HUS Helsinki University Hospital, from its very beginning. We interviewed Aleksi Kuitunen, who has served as HUS account manager for ten years, as well as technology strategist Olli Rysä. Both work within the CHN ecosystem, advancing healthcare projects in cooperation with other partners.
Artificial intelligence and cloud services at the heart of healthcare
Microsoft’s role in healthcare has evolved significantly in recent years, especially with the use of artificial intelligence and cloud services. Aleksi Kuitunen explains how the company has integrated artificial intelligence into all its products. This will soon be seen concretely in the everyday work of healthcare professionals. Microsoft offers platform and management competences for the AI models of several suppliers, and its partnership with OpenAI enables language models to be brought rapidly into customer use.
One example of Microsoft’s AI solutions is the Dragon Copilot developed for healthcare professionals.
It automates clinical documentation and other routine tasks by combining speech recognition, ambient listening and generative artificial intelligence into one seamless workflow. In safe healthcare AI solutions, there is much more at stake than the large language models that have attracted most of the attention. In addition to large language models, Microsoft develops AI models specialised for specific industries and use cases, as well as the technologies required for the safe utilisation of these AI models.
One example of AI solutions intended for clinical applications is GigaTIME AI model. Artificial intelligence is also used for the routine analysis of pathological images, which enables more precise and cost-effective diagnostics when more detailed and much more expensive imaging is required. In addition, prototypes such as MAI-DxO have been developed for orchestration layers, where AI agents diagnose illnesses and support clinicians in complex and challenging patient cases.
Strong local presence and global investment
Microsoft’s global presence is also visible in Finland, where investments in local presence is ongoing. The company invests heavily in capacity availability in various countries, and one of the most sizable data centre clusters in Europe is currently being built in Finland. Olli Rysä points out that these projects also benefit healthcare systems, increasing their safety and resilience which is important in the current global situation. Globally, the scale of the investments is significant: in the previous quarter, Microsoft invested more than USD 35 billion in the development of the data centre network and cloud service platforms.
The data centre area being built in Espoo, Kirkkonummi and Vihti focuses on sustainability: Microsoft has committed to covering the equivalent of its own electricity consumption with new, renewable energy capacity through power purchase agreements. The residual heat from the Espoo and Kirkkonummi data centres will be recycled into district heating for the local district heating network.
Microsoft is also a leading player in cyber security, protecting Europe’s cyber defences. The company is one of the world’s largest research organisations and invests tens of billions in research activities. This is also strongly reflected in the development of healthcare solutions.
Cooperation in the ecosystem and local solutions
Microsoft works closely through its partner network in Finland as well. Its collaboration partners include, among others, application development companies that create patient information systems, transaction services and other sectorspecific solutions for healthcare as well as other industries. Partners also include solution companies who deploy and maintain applications tailored to customer needs. Local partner cooperation ensures that the solutions adapt to local regulations and processes and meet the needs of end users.
CHN projects make use of HUS data platform, which operates within Microsoft’s services. This HUS data platform has enabled the development of CHN projects by providing a safe and highquality way to manage large datasets. For example, the HUS Academic cloud environment is certified by HUS itself and can be scaled to meet research needs. Many CHN network partners also develop new solutions based on Microsoft’s technology platforms.
The central idea of CHN, the power of collaboration, aligns well with Microsoft’s operating principles. Aleksi Kuitunen notes: “Finland is a small country, and we need to combine different perspectives to bring worldclass solutions into use. The multidisciplinary expertise and collaboration within the CHN ecosystem enable progress and the creation of innovations.”
Cooperation and innovation culture between companies of all sizes
Microsoft collaborates with both large and small companies. Large players bring critical expertise in cyber security and scalability, while smaller companies are often more agile and innovative. Combining these strengths is the key to success.
Aleksi Kuitunen emphasises the role of artificial intelligence in the culture of development and experimentation. With artificial intelligence, innovation processes can be accelerated, prototypes quickly produced and risks reduced. Microsoft already has experience in developing a project idea with HUS by using a Copilot Researcher agent. Development ideas can be further elaborated using an AI developer, achieving prototype phase in just one day. Harnessing artificial intelligence enables more straightforward dialogue between end users and development partners and faster development work.
Microsoft’s investments in artificial intelligence, cloud services and the partner ecosystem open up new opportunities for the development of healthcare and for scaling up new innovations globally. Artificial intelligence helps in the ideation, productisation and practical implementation of solutions and resources can be allocated more efficiently to the research problem itself or users’ challenges, rather than to application development.
