AI-Based workflow implementation

  • AI-Based workflow implementation

When asked about their expectations for a good PACS, radiologists today emphasize the importance of excellent imaging workflow. In the age of artificial intelligence, this focus includes seamless integration of AI applications and their uses within the PACS. The JiveX AI workflow is designed to meet this specific requirement.

When it comes to the routine use of AI applications in healthcare processes, radiology plays a pioneering role – just as it does with digitalization in general. It became apparent early on that AI applications are excellent for evaluating radiological images. For example, the use of AI has now made it possible to record and assess brain volume in a reasonable amount of time, which has taken dementia diagnostics an important step forward. Furthermore, evidence now shows that volume progression based on AI measurements in tumor diseases allows more precise statements about the course of various diseases and treatment responses, thereby providing added therapeutic value to the entire process. 

Creating added value by bridging gaps with JiveX

Simply “acquiring” AI isn't enough for it to generate the added value described above. More AI technology means more data and complexity for radiologists. Efficient AI use is crucial, and this efficiency is primarily achieved through integration into the PACS’ workflow. To facilitate this, the JiveX AI Workflow acts as a bridge between the JiveX Enterprise PACS and specific AI applications. This bridge allows image data and AI evaluations to bypass individual IT solution limitations effortlessly, allowing the entire diagnostic workflow to proceed seamlessly within the PACS.

Thanks to the JiveX AI Workflow bridge, JiveX Enterprise PACS users can effectively integrate the multitude of new AI tools into their existing workflows. However, to ensure an efficient workflow, it is equally important that the results generated by the AI flow back into the PACS and are displayed here in such a way that the radiologists receive treatment-relevant information. At present, this functionality is available for examinations that yield clearly defined results by adhering to established standards. Examples include mammography, orthopedic measurements, and thoracic examinations. 

We help shape standardization

We are actively promoting the development of additional standards to enable the integration of more AI solutions into PACS-based workflows. The Interest Group for AI in Imaging, founded under the “IHE” umbrella organization, comprises AI manufacturers, users, and other stakeholders. This group aims to incorporate standard profiles and formats into AI development from the start, facilitating seamless integration into existing software like PACS. This effort is crucial as AI applications expand into other medical areas, ensuring tangible benefits for care. In radiology, preparations for this development are underway, with our team leading the initiative.


These blog articles might interest you

Artificial intelligence (AI) in radiology

What radiology can expect from AI

Nowadays, discussing artificial intelligence (AI) in radiology is pretty neutral – there aren’t any overly euphoric or doomy-gloomy reactions. That’s a good thing because, now that tempers have calmed down and debates have become more objective, it’s finally possible to talk about the true potential and limitations of AI. For example, a healthy debate on the topic recently took place with Dr. Elmar Kotter, President of the European Society of Medical Imaging Informatics and Chair of the “eHealth and Informatics Sub-committee” at the European Society of Radiology.

Artificial intelligence in the VISUS strategy

Artificial Intelligence in VISUS’ Strategy

For many years, the topics of data consolidation, archiving, and distribution have been part of VISUS developers’ agenda. This has laid an important foundation for further developing medicine as a whole and radiology in particular. The consolidation of information, its precise availability, and secure and intelligent storage thereof are important building blocks for a practical application of Artificial Intelligence (AI) solutions...