Research group
INSIGHT – Intelligent Sensing and Computing Technologies
We specialize in intelligent measurement and calculation technologies that are located at the interface of computational and biological sciences.
At INSIGHT, we specialize in intelligent measurement and calculation technologies that are located at the interface of computational and biological sciences. Our solutions drive innovation in the fields of medical technology and health and wellbeing technologies. We focus on developing advanced computational methods – such as distributed machine learning for medical imaging – to support diagnostics, patient monitoring and clinical decision-making.
Our key competences
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Privacy-Driven Generative AI and Distributed Machine Learning
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Machine Vision and Machine Learning in Medical Imaging
Infrastructure for research and development
INSIGHT Lab
INSIGHT Lab is a health and sport technology testbed environment. In its operations, we utilize e.g., biosensory technology, data gathering, analysis software packages, machine learning and augmented reality. In the INSIGHT Lab we develop digital healthcare services and carry out product development of medical devices together with the health sector’s organisations and companies. The laboratory’s equipment and expertise are available to our partners in joint research and development projects. The lab also works as learning environment, where students from the field of information and communications technology complete their studies.
Research Group Lead
Our Experts
The INSIGHT research group and the staff at INSIGHT Lab includes professionals in the field of engineering and health, with dozens of years of experience in the field in total.
Most of our experts have defended their doctoral dissertations on topics related to artificial intelligence or programming. The staff has been involved in several research, product development and service projects and offered tailored education in the field of health technology to support organizations.
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News
Finland’s first synthetic health data test platform ready – providing researchers with real-world health data
The joint project has created Finland’s first test platform for synthetic health data, which enables the use of real data in the early stages of research without any privacy risks. The platform, developed in collaboration with health actors in the region, will help to combine data sources and develop new health concepts. Product development in the health sector needs high-quality data. Artificially generated data are suitable for limited uses, while the use of real health data is highly regulated.
We continuously have several extensive research and development projects underway with our partners in Finland and internationally. Some of our projects are Horizon funded, and the results have also been patented.
Main publications
Shopland, N. et al. (2026). Towards Inclusive AI System Development for Disease Risk Prediction: Collecting, Prioritising and Incorporating User Stories from Heterogeneous Stakeholders. In: Duffy, V.G., Gao, Q., Zhou, J. (eds) HCI International 2025 – Late Breaking Papers. HCII 2025. Lecture Notes in Computer Science, vol 16340. Springer, Cham. https://doi.org/10.1007/978-3-032-13022-8_27
Tryykilä, P., & Kontio, E. (2025). Ecosystem supporting the commercialization of digital health innovations. Finnish Journal of EHealth and EWelfare, 17(1). https://doi.org/10.23996/fjhw.154958
Khan, M. I., Kontio, E., Khan, S. A., & Jafaritadi, M. (2025). Federated Brain Tumor Segmentation Using Bayesian Similarity-Weighted Aggregation. Neural Information Processing, 61–73. https://doi.org/10.1007/978-981-96-6969-1_5
Linardos, A., Pati, S., Baid, U., Edwards, B., Foley, P., Ta, K., Chung, V., Sheller, M., Khan, M. I., Jafaritadi, M., Kontio, E., Khan, S., Mächler, L., Ezhov, I., Shit, S., Paetzold, J. C., Grimberg, G., Nickel, M. A., Naccache, D., … Bakas, S. (2025). The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods. Machine Learning for Biomedical Imaging, 3(December 2025), 757–774. https://doi.org/10.59275/j.melba.2025-5242
Zenk, M., Baid, U., Pati, S., Linardos, A., Edwards, B., Sheller, M., Foley, P., Aristizabal, A., Zimmerer, D., Gruzdev, A., Martin, J., Shinohara, R. T., Reinke, A., Isensee, F., Parampottupadam, S., Parekh, K., Floca, R., Kassem, H., Baheti, B., … Bakas, S. (2025). Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge. Nature Communications, 16(1). https://doi.org/10.1038/s41467-025-60466-1
Kontio, E., & Salmi, J. (2024). Democracy and Artificial General Intelligence. Human Factors, Business Management and Society. https://doi.org/10.54941/ahfe1004960
Ranttila, P., Sahebi, G., Kontio, E., & Salmi, J. (2024). Medical AI in the EU: Regulatory Considerations and Future Outlook. Artificial Intelligence – Social, Ethical and Legal Issues. https://doi.org/10.5772/intechopen.1007443
Jafaritadi, M., Teuho, J., Lehtonen, E., Klén, R., Saraste, A., & Levin, C. S. (2024). Deep generative denoising networks enhance quality and accuracy of gated cardiac PET data. Annals of Nuclear Medicine, 38(10), 775–788. https://doi.org/10.1007/s12149-024-01945-1
Pitkämäki, T., Pahikkala, T., Perez, I. M., Movahedi, P., Nieminen, V., Southerington, T., Vaiste, J., Jafaritadi, M., Khan, M. I., Kontio, E., Ranttila, P., Pajula, J., Pölönen, H., Degerli, A., Plomp, J., & Airola, A. (2024). Finnish perspective on using synthetic health data to protect privacy: the PRIVASA project. Applied Computing and Intelligence, 4(2), 138–163. https://doi.org/10.3934/aci.2024009
Khan, M. I., Azeem, M. A., Alhoniemi, E., Kontio, E., Khan, S. A., & Jafaritadi, M. (2023). Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 121–132. https://doi.org/10.1007/978-3-031-44153-0_12
Schultz, J., Siekkinen, R., Tadi, M. J., Teräs, M., Klén, R., Lehtonen, E., Saraste, A., & Teuho, J. (2022). Effect of respiratory motion correction and CT-based attenuation correction on dual-gated cardiac PET image quality and quantification. Journal of Nuclear Cardiology, 29(5), 2423–2433. https://doi.org/10.1007/s12350-021-02769-6
Gambin, J. R., Tadi, M. J., Teuho, J., Klen, R., Knuuti, J., Koskinen, J., Saraste, A., & Lehtonen, E. (2021). Learning to Denoise Gated Cardiac PET Images Using Convolutional Neural Networks. IEEE Access, 9, 145886–145899. https://doi.org/10.1109/access.2021.3122194
Khan, M.I., Jafaritadi, M., Alhoniemi, E., Kontio, E., Khan, S.A. (2022). Adaptive Weight Aggregation in Federated Learning for Brain Tumor Segmentation. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_40
Elina Kontio, Antti Airola, Tapio Pahikkala, Heljä Lundgren-Laine, Kristiina Junttila, Heikki Korvenranta, Tapio Salakoski, Sanna Salanterä. Predicting patient acuity from electronic patient records, Journal of Biomedical Informatics. Volume 51,
2014, Pages 35-40, ISSN 1532-0464, https://doi.org/10.1016/j.jbi.2014.04.001.
M. I. Khan, E. Alhoniemi, E. Kontio, S. A. Khan and M. Jafaritadi, “RegAgg: A Scalable Approach for Efficient Weight Aggregation in Federated Lesion Segmentation of Brain MRIs,” 2023 Eighth International Conference on Fog and Mobile Edge Computing (FMEC), Tartu, Estonia, 2023, pp. 101-106, https://doi.org/10.1109/fmec59375.2023.10306171
Félix J, Moreira J, Santos R, Kontio E, Pinheiro AR, Sousa ASP. Health-Related Telemonitoring Parameters/Signals of Older Adults: An Umbrella Review. Sensors. 2023; 23(2):796. https://doi.org/10.3390/s23020796
Degree programmes related to our operations
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Bachelor's Degree or Path Studies
Information and Communications Technology, Bachelor of Engineering
Full-time StudiesTurku
Network & partners
The cooperation partners of the research lab include companies, hospital districts, municipalities, health care organisations, third sector actors, higher education institutions and research and development units. The research group in Health Technology is active in cooperating with the operators of Health Campus Turku and Tech Campus Turku in issues related to health technology. We also collaborate with the Department of Computing at the University of Turku and the Wellbeing services county of Southwest Finland.
We are also a partner of the Health Campus Turku knowledge cluster and TERTTU collaboration platform, which brings together all the expertise and co-creation services at the campus through one channel.
Strong project skills
Turku University of Applied Sciences’ Project Office offers support and guidance throughout the life cycle of an RDI project. Our project experts have years of experience and strong expertise in national and international funding programmes. We have more than 200 projects running every year, and our RDI activities have received more than €10 million in external funding.