As part of the ongoing digitalization of the healthcare system, the clients are procuring a radiological Clinical Decision Support System (CDSS) based on the Hospital Future Act (KHZG).
The CDSS will meet the requirements of funding 4 (FTB4) of the KHZG and the EU Medical Device Regulation (MDR) and support clinical decision-making by analyzing extensive health data. The aim is to maximise the medical benefit for patients while complying with legal requirements.
The cloud-based CDSS provides trained AI algorithms that are able to consistently recognize the finest details in images or image series. By using such algorithms in parallel, the risk of human error can be reduced. For example, the detection of acute or incidental pulmonary artery embolism by AI is faster and, especially in CT examinations for other purposes, also safer than human reporting alone. Due to the usually very fast processing time, the time between image acquisition and diagnosis can be shortened, as the AI algorithm can detect abnormalities reliably and quickly. AI-based assistance systems thus actively contribute to the prioritization of critical cases. Examinations with relevant abnormalities can be prioritized in the diagnosis, which is a great advantage, especially in emergency situations or with a high workload. Especially in the case of acute vascular occlusion of the vessels supplying the brain or intracranial hemorrhage, early detection and classification by AI can have a decisive influence on the speed of diagnosis and treatment.
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Introduction Radiological Clinical Decision Support System.
As part of the ongoing digitalization of the healthcare system, the clients are procuring a radiological Clinical Decision Support System (CDSS) based on the Hospital Future Act (KHZG).
The CDSS will meet the requirements of funding 4 (FTB4) of the KHZG and the EU Medical Device Regulation (MDR) and support clinical decision-making by analyzing extensive health data. The aim is to maximise the medical benefit for patients while complying with legal requirements.
The cloud-based CDSS provides trained AI algorithms that are able to consistently recognize the finest details in images or image series. By using such algorithms in parallel, the risk of human error can be reduced. For example, the detection of acute or incidental pulmonary artery embolism by AI is faster and, especially in CT examinations for other purposes, also safer than human reporting alone. Due to the usually very fast processing time, the time between image acquisition and diagnosis can be shortened, as the AI algorithm can detect abnormalities reliably and quickly. AI-based assistance systems thus actively contribute to the prioritization of critical cases. Examinations with relevant abnormalities can be prioritized in the diagnosis, which is a great advantage, especially in emergency situations or with a high workload. Especially in the case of acute vascular occlusion of the vessels supplying the brain or intracranial hemorrhage, early detection and classification by AI can have a decisive influence on the speed of diagnosis and treatment.