AIQNET

Artificial Intelligence for Clinical Studies – sub-project: Development of a digital patient model for diagnosis and treatment decision support

Due to the new Medical Device Regulation of the EU, the requirements for reliable medical device data are continuously increasing. To prove their continuous safety and performance, a market observation of the products based on clinical studies is explicitly required, which also enables a comparative quality and performance evaluation. The market monitoring is expected to optimize the performance data of medical devices, but also to improve diagnosis and medical treatment. The acquisition, storage, and analysis of clinical data in compliance with the data protection regulations are essential for this evaluation. Currently, the market surveillance is often characterized by a lack of skilled personnel in industry and hospitals, cost pressure, legal uncertainty and IT systems with low interoperability.

These aspects will be addressed in the AIQNET project (Artificial Intelligence for Clinical Studies). With the help of artificial intelligence epidemiological, clinical, para-clinical and radiological data can be automatically analyzed and used for the performance evaluation of medical devices.

Therefore, a digital ecosystem that ensures compliance with legal and ethical frameworks through state-of-the-art architecture and security technologies will be conceptualized and developed in the project. Within the framework of the platform, a database will be created, which enables both the clinics and medical device manufacturers to use clinical data for research and development effectively and in compliance with the EU regulatory framework.

As part of the sub-project, ICCAS will develop a “Digital Patient Model” that integrates different perspectives on treatment and the patient. The aim is to improve AI-supported clinical quality and performance assessment based on previous knowledge about the patient and the treatment. This includes aspects of diagnosis and the disease, information on therapies and the specific characteristics of the patient.

Main project website