AI For Health
Breakthroughs in personalized medicine are expected to revolutionize medical care and have a strong long-lasting societal impact. AI will play a central role in this process and it is already making the first steps into the clinics. This page highlights how each of our research program interacts with healthcare research within FCAI and lists the groups currently involved.
Coordinator: Professor Simo Särkkä – firstname.lastname@example.org
Agile Probabilistic AI provides explainable, verifiable, uncertainty-aware, and reliable tools for healthcare data analysis. The tools enable fast deployment of the healthcare AI solutions while providing rigorous methods for validation and uncertainty quantification. These tools will benefit the public and private sector users of the AI by improving the outcomes of healthcare interventions, and will also benefit the medical researchers aiming to find solutions to fundamental challenges in diagnosing and treating human health.
Simulator-based Inference enables the use of established knowledge in human health as part of personalized healthcare AI. Examples of knowledge that might be added to the AI simulations include models of human physiology and disease evolution. Including these models helps the AI to learn and generalize faster and better when using the smaller data sets that are typically available when treating an individual. The models are efficient and their reasoning is interpretable by a human reader. The models are even capable of learning causal relations by combining observations with experiments that the AI planned autonomously, which has a huge potential in modern medical research.
Next-Generation Data-Efficient Deep Learning boosts the already ongoing success of deep learning in healthcare data analysis by providing AI methods that can cope with less training data while still sustaining the full power of deep learning. This ability to develop solutions with less initial data, especially in combination with simulator-based inference, allows for harnessing deep learning methods in many healthcare applications where the data is expensive or laborious to collect.
Privacy-preserving and Secure AI provides AI solutions for healthcare, which preserve the privacy of healthcare data as well as the security and privacy of the developed AI systems. These kinds of solutions are particularly important in medical data analysis where ethical aspects as well as regulations such as GDPR have to be taken into account. Public sector healthcare data is an example of a valuable data source that cannot be utilized in AI solutions without proper privacy and security considerations.
Interactive AI ensures the natural interaction between the healthcare AI solutions, and the patients and healthcare providers. It allows for AI that patients and medical personnel feel comfortable to use, trustworthy, and worth using in practice. Developing patient and clinican trust in the system is essential for acceptance of AI solutions as day‑to‑day tools in medical practice.
The following groups already take part in FCAIs AI for Health research. The list is currently under construction. If your group is already involved and needs listing here, please contact us.
Sture Andersson, University of Helsinki
Jukka Corander, University of Helsinki, University of Oslo and Sanger Institute
Simulator-based inference, ABC, Population genomics, Vaccine development, Antibiotic resistance, Infectious disease epidemiology
Jaakko Hollmen, Aalto University
Antti Honkela, University of Helsinki
Leo Kärkkäinen, Nokia Bell Labs, Aalto University
Efficient, embedded, ways to utilize deep learning for medical data, Physical simulations to produce datasets for deep learning, GAN-based methods for improving transfer learning from simulations and for anonymizing datasets, Active learning
Kimmo Kaski, Aalto University
Samuel Kaski, Aalto University
Harri Lähdesmäki, Aalto University
Bioinformatics, Personalized medicine, Probabilistic modelling, Machine learning
Pekka Marttinen, Aalto University
Probabilistic modeling, Machine learning, Electronic health records, Statistical genetics, Mobile health
Lauri Parkkonen, Aalto University
Matti Pirinen, University of Helsinki
Statistical genetics, Population genetics, Variable selection, Multivariate data analysis
Samuli Ripatti, University of Helsinki
Juho Rousu, Aalto University
Machine learning, Predicting structured data, Computational metabolomics, Personalized medicine, Systems pharmacology and pharmacogenomics, Systems and synthetic biology
Riitta Salmelin, Aalto University
Simo Särkkä, Aalto University – Coordinator
Sensor and health informatics, Cardiovascular, AI multi-sensor medical diagnostics, AI for medical imaging, Biomedical sensing, Brain imaging
Arno Solin, Aalto University
Aki Vehtari, Aalto University
Bayesian data analysis, Probabilistic programming, Computational statistics