FLORENCE
1 out of 4 patients experience complications after colon cancer surgery. A new research project enhances the treatment of patients using artificial intelligence and federated learning.
In project FLORENCE, this challenge in the program region will be solved through the use of advanced technology. A tool based on artificial intelligence (AI) is being developed to enhance the diagnosis, prognosis, and treatment of patients with colon cancer.
The project’s utilization of advanced technology in colon cancer surgery is an innovative approach that creates opportunities for new partnerships and collaborations across borders. The primary target audience is healthcare professionals working in or interested in colon cancer surgery at hospitals within the program region. Other target groups in the project include companies, patients with colon cancer, as well as politicians and other stakeholders..
The FLORENCE project utilizes the OMOP Common Data Model, which is a leading approach for creating data infrastructures that promote the use of personalized medicine in healthcare (i.e., tailored treatment for individual patients).
“By using federated learning, the project will, for the first time globally, directly link the AI model to clinical practice. The project is therefore unique in its approach to addressing a healthcare challenge for the patient group.”
The AI tool is designed to assist healthcare professionals in optimizing the decision-making process for patients with colon cancer. This means that it can enhance the quality of life for the citizens. On a health economic level, it also means that expenses related to readmissions and surgeries can be reduced
enabling citizens to return to the workforce after the surgery.
Therefore, the FLORENCE project will benefit healthcare professionals in the program region who are involved in colon cancer surgery and the patients they treat. In the long run, it can also inspire hospitals outside the program region to use the AI tool in connection with colon cancer surgeries.
FLORENCE is an abbreviation for central activities in the project (i.e., federated learning with OMOP modeling of health data to improve colon cancer treatment in the Nordic region).
Project goals
Improve patient treatment
The project will enhance the treatment of patients with colon cancer by utilizing AI and federated learning.
Best practice for working with federated learning
An international best practice for implementing registry-based AI models in clinical practice will be established.
To strengthen the collaboration between businesses and the healthcare sector
Companies are strengthened in their competitiveness with new knowledge about AI in healthcare technology and federated learning, as well as the utilization of patient data across borders.