AI-supported diagnostics for ME/CFS to improve diagnostic processes and resource utilisation in healthcare (DETECT-ME/CFS)

Project description

The DETECT-ME/CFS project aims to enable rapid and reliable diagnosis of ME/CFS in specialised facilities with limited resources and a high workload.

The project is developing an innovative clinical decision support system (CDSS) that supports clinical expertise and decision-making processes with the help of artificial intelligence (AI). Diagnostic accuracy and efficiency are to be increased in two ways: By analysing anonymised patient data, AI will be used to identify patterns and new diagnostic features to reliably exclude differential diagnoses. Second, a system based on automated literature search for ME/CFS and established differential diagnoses will be created to improve overall diagnostic capabilities using machine learning. The CDSS will be tested in practical clinical practice and incorporates automated closed-loop learning mechanisms for its continuous improvement.

If successful, the resulting AI-based innovative support system can significantly improve the diagnostic accuracy and efficiency of ME/CFS in routine clinical practice, thus saving essential resources and relieving the burden on specialized outpatient clinics. In the long term, the model can also be used for the clinical diagnosis of other complex diseases and represent a significant advance in healthcare overall.

Description adapted from project website: see link above.