Our research and passion

Cooperation and interactions between humans, knowledge, information and data

Human and machine cooperate. The machine becomes intelligent as it understands the language of humans and knows how to interprete the meaning of concepts. The machine knows today how to communicate and interact with humans.

Our passion is to work on the interactions between humans and machines, on the information and data exchanged and more specifically on their meaning. Processing this data and its meaning allows to combine the processing power of machines with the skills of human intelligence.

Our group is affiliated to the Department of Radiology and Medical Informatics, at the UNIGE Faculty of Medicine. This Department covers a very large field of research in the field of imaging, radiology and digital health, from fundamental research to clinical applications in prevention, diagnosis, therapy and interventions as well as prognosis.

The data team

Data, information, knowledge and artificial intelligence 

Data and information are two pillars of medicine growing exponentially. Huge amounts of data are generated at every moment, for instance thousands of new publications are indexed every day. The ability to translate these information flows into relevant and usable knowledge is a major challenge, particularly to improve medical knowledge and our understanding of mechanisms, the development of our prevention and prediction tools. Furthermore, this process allows the implementation of new diagnostic and therapeutic approaches and the measurement of their impact.

However, data in health and medicine presents many challenges. It is multimodal: made up of text, such as reports or scientific articles; of images such as x-rays; signals such as electrocardiograms; of numbers; or forms only to cite a few examples. Data are also heterogeneous as they come from many different sources. Finally, they are temporal, because most data vary over time for the same person, for the same population. They are in flux, constantly moving.

The work done by the Data team is based on two main axes: 

    • Knowledge management and representation: Conceptual representations, in particular graphs, allow to explicit the interrelations and diverse formal rules underlying to the data. This requires a lot of resources with multiple skills to annotate, label, organize the knowledge and metadata of the sources we process. This organization of knowledge into globally interoperable data sources, enriched with metadata and semantics, allows us to create a kind of network of truths, a formal and reliable knowledge.  
    • Data analysis and hybrid artificial intelligence: To derive knowledge and usable information from data flows, we use numerous formal, probabilistic and algorithmic analytic and machine learning strategies to support applied and translational research activities, such as our work on the impact of public health measures on the COVID pandemic (HUG chart: see our covid chart and publication).

See more about the Data team

The EvaLab team

User-centered design, human-machine interactions and human factors engineering

Everyday our interactions with digitized systems are strengthening: mobile phones, apps, Internet, and other data visualization means. These tools have become ubiquitous and indispensable.  

The EvaLab is particularly interested in understanding and developing interaction paradigms adapted to precise contexts (hospital, home, ...) and specific users (patients of all age, healthcare professionals, decision-makers or citizens). This implies user-centered approaches, to place end-users at the core of the development process to clearly identify their needs and capacities in order to address them. The aim being to guarantee the development of easy to use, effective and efficient solutions.  

See more about the EvaLab team

Research, development and teaching

New life sciences PhD school

In all these fields, we are developing a strong research and teaching activity, notably in coordination with the Faculty of Medicine and the Faculty of Science of the University of Geneva,  with a structured training program that begins in pre-graduate training for bachelor and master degrees in medicine, and the obtaining of doctorates in medicine or PhDs in life sciences, "genomics and digital health". We also organize postgraduate and continuing education, including a CAS and a MAS in medical informatics. 

See more about our teaching programs