• News
  • People
  • Long Read
  • Opinion
  • Weekend Wrap

News Spotlight

AI powers healthy pursuits in Finland

MVision AI has secured 5.4 million euros in funding for scaling up its artificial intelligence-based solution for planning radiotherapy treatment.

MVision AI

Artificial intelligence and machine learning are powering efforts to bioengineer new enzymes, expedite drug development and improve access to radiotherapy.

Orion in March announced it has set out on a four-year project to build a cutting-edge ecosystem for pharmaceutical research in Finland.

Consisting of companies, universities and research institutes, the ecosystem will utilise artificial intelligence and machine learning in order to reduce the time required for studying and developing pharmaceutical products.

“Utilising data with the help of artificial intelligence is a competitive advantage for developing new innovative medicines because it expedites development and significantly increases the probability of success,” told Outi Vaarala, director of innovative medicines at Orion.

The Finnish pharmaceutical giant estimates that the ecosystem could expedite pharmaceutical product development by as much as three years.

Finland’s Orion has started building a pharmaceutical research ecosystem that, it believes, could expedite the drug development process by as much as three years.

Karolina Grabowska / Pexels

The ecosystem operations will be founded on sharing information, taking advantage of the variety of expertise in the ecosystem and creating the data required for validating new predictive models. By bringing together the expertise of disease biologists, pharmaceutical researchers and data scientists, the pharmaceutical projects launched during the project will not only create new capabilities but also create international business opportunities for the participating companies, according to Orion.

“The amount of information is constantly growing, and utilising it optimally will increasingly require resources and special expertise. Finland has a lot of high-level knowhow, but the competence is scattered,” said Vaarala.

“Finnish pharmaceutical, biotechnology and technology companies can succeed in the tough global competition by joining forces. A functioning and flourishing research ecosystem for the pharmaceutical industry will also attract talent in the industry and investments to Finland,” she envisioned.

Business Finland has granted Orion 10 million euros in funding and allocated another 20 million euros for other members of the ecosystem.

Aalto and VTT start potentially transformative work on enzymes

Scientists at Aalto University and VTT Technical Research Centre of Finland have embarked on a project that seeks to accelerate the transition toward circular bioeconomy by creating new enzymes with the help of machine learning.

Intelligent bioengineering is expected to contribute to the transition by replacing many existing processes that are based on fossil raw materials, Merja Penttilä, research professor at VTT, elaborated in March.

“We can design and engineer novel cells to produce basically any useful product, and enzymes are key players to make this happen,” she said.

“Our goal is to create totally new enzymes that are capable of transcending natural evolutionary principles and maximising their industrial utility. If we succeed, we will revolutionise possibilities to transition from a fossil-based economy to a circular bioeconomy.”

Staff from Aalto University and VTT Technical Research Centre of Finland attended the kick-off meeting of the BioDesign project in March.

Matti Ahlgren / Aalto University

Penttilä leads the BioDesign project with Samuel Kaski, professor at Aalto University. The project has received almost two million euros in funding from Jane and Aatos Erkko Foundation.

The biological catalysts of all things living, enzymes are complex proteins that are widely used in industrial biotechnological processes, such as the production of antibiotics, bioethanol, bioplastics and medicines. While Aalto University and VTT believe the potential of biological diversity is far from fully utilised, they acknowledge the extent of work required to discover and engineer proteins that can serve as enzymes with specific functions or properties.

Consisting of experts in molecular biology, synthetic biology, machine learning and computer science, the research team has set out to construct a fully virtual cycle for enzyme engineering that facilitates fruitful collaboration between scientists and artificial intelligence. The outcomes of the virtual simulations will be tested in real-world laboratories to generate experimental data, which is fed back into the simulation to increase learning and prediction power.

“New enzymes discovered through this cycle could not only speed up current chemical reactions, but also enable alternative biological pathways toward synthesising better materials, biofertilisers and drugs,” stated Vikas Garg, assistant professor at Aalto University.

Garg said he believes the project could be as transformative as generative language models such as ChatGPT.

Finland, the project partners believe, is well-positioned to push the bounds of synthetic biology given its knowhow in both molecular biology and artificial intelligence. A broader aim of the project is duly to keep the country at the vanguard of the field, said Juho Rousu, professor of computer science at Aalto University.

Speeding up access to radiotherapy

MVision AI, a Helsinki-based health technology startup, has secured 5.4 million euros in funding for scaling up its artificial intelligence-based solution for planning radiotherapy treatment. The solution promises to improve access to cancer treatment with automated segmentation that helps to standardise contouring and streamline the planning workflow.

The post-seed funding round was backed by J12 Ventures and Voima Ventures.

With CEO Mahmudul Hasan at the helm, MVision's solution has already been used to begin treatment for more than 100 000 patients in 14 countries.


“The burden of cancer is growing for patients, hospitals and carers,” stated Mahmudul Hasan, CEO of MVision AI. “Our cloud-based AI-powered technology provides faster and more reliable clinical decision making for cancer patients undergoing radiotherapy treatment.”

“With our automation, we can speed up the entire treatment planning process while still delivering high-quality care.”

The difference to manual planning is potentially stark: where today patients may have to wait for two to three weeks for treatment due to inefficient planning processes and overburdened healthcare systems, the automated solution aims to enable same-day treatment.

The speed of treatment can be decisive for cancer patients, reminded MVision AI.

The solution has already been used to begin treatment for more than 100 000 patients in 14 countries, including Finland, France, Spain, Sweden the UK and the US. The quality and consistency of the automated segmentation was immediately apparent, attested Chihray Liu, professor and chief physicist at the University of Florida’s Gainesville Department of Radiation Oncology.

“It fits seamlessly and transparently into our workflow, saving many planners and physicians time,” he said.

Digital double-up

Business Finland recently drew attention to the potential of Finnish health data by reporting on a collaborative project between Finland’s BCB Medical and Germany’s Fraunhofer.

Fraunhofer tapped BCB Medical to gather data required for testing a digital twin developed to support physicians treating inflammatory bowel disease, a group of potentially debilitating gastrointestinal conditions. Covering all of the 170 variables required by Fraunhofer, the data is expected to provide more insights and enable the company to move on from testing to commercialising the digital twin as a tool that supports clinical decision-making.

“This is also critical for Finland,” reminded Lisse-Lotte Hermansson, chief scientific officer at BCB Medical. “We are very proud of our great data, but only by testing it in these types of algorithms we can really see if the data is good enough.”

The twin being a general-purpose data model, it could prove useful for treating a number of other diseases, too.

By: Aleksi Teivainen