Funding for the joint Research Training Group (RTG) of Fachhochschule Dortmund and the University of Duisburg-Essen continues: The German Research Foundation (DFG) is supporting the programme "Knowledge- and Data-based Personalization of Medicine at the Point of Care" (WisPerMed for short) for an additional four and a half years.
Digitalization in medicine generates a large amount of clinical data. The RTG makes it usable for doctors in a new form. The aim of the programme, which has been running since 2021, is to use AI to advance personalized medicine directly where patients are treated (point of care). Using the example of malignant melanoma, the projects at Essen University Hospital are developing new tools for this. Personalized medicine refers to both sides: Instead of the principle of "one treatment for all", medical decisions are made based on data and tailored in each case to the biological, health and personal situation of sufferers. On the other hand, the individual preferences of the treating physicians are also taken into account. This is because they need to understand the information quickly and intuitively when using the tools.
"There is an explosion of knowledge in medicine, especially in oncology; more and more data is being generated. Doctors have neither the time nor the capacity to filter and process everything themselves," explains GRK spokesperson Prof. Dr. Felix Nensa, an expert in radiology with a focus on AI at the Faculty of Medicine at the University of Duisburg-Essen. "We therefore want to provide them with additional knowledge and generate new knowledge from data without restricting their freedom to make decisions." This is a huge opportunity, especially in cancer medicine.
Optimization of processes
"In the upcoming funding phase, research will be extended to the entire patient treatment pathway," says deputy RTG spokesperson Prof. Dr. Christoph M. Friedrich, an expert in biomedical informatics at Fachhochschule Dortmund. "Instead of addressing individual decision support for specific problems as before, the new approach aims to holistically record, support and optimize processes from initial diagnosis to treatment and aftercare."
By seamlessly integrating patient data and clinical knowledge at various interfaces in the healthcare system, the aim is to create technologies that both meet the individual requirements of healthcare professionals and improve continuity of care and the patient's treatment experience. In the second funding phase, patients will now also be more involved, for example with patient-oriented summaries of doctors' letters.
Background to the Research Training Group
In the RTG, 13 professors and 13 doctoral students are currently researching an adaptive system for integrating AI into medical decision-making processes. Using machine learning methods, among other things, data is intelligently linked and systematically evaluated: data from diagnostic guidelines, treatment and aftercare, all available knowledge from studies, patient databases and all relevant data on the patient. The AI could then generate a treatment recommendation and predict whether a tumor could develop resistance or a therapy could have serious side effects. Doctors can always see the basis on which the recommendation was made in order to monitor the results.
The results of the AI are visualized in a dashboard - tailored to the personal working methods and faculties of the treating physicians. To achieve this, the doctors work together with other disciplines such as computer science and social psychology.