The proprietary AI-X-Platform developed by Exploris enables rapid development of new kinds of applications in healthcare while utilizing a combination of modern AI methods and various algorithms for unrivaled accuracy and flexibility.
The eDiagnostic services of Exploris will transform the existing processes in today’s medical care for the benefit of patients. The team’s close collaboration with senior practitioners in leading hospitals is impressive and really reflects our culture of co-creation.
In more than 10 years of research and clinical development we, together with our partners and in collaboration with clinics, have developed a new approach and the related Artificial Intelligence based software environment. Based on clinical data new methods and tools have been derived, exceeding the capabilities of so far available software.
We already have prominent examples unveiling the potential of our AI-X-Platform. Beside the Cardio Explorer test which detects stenosis in coronary arteries better than for example the stress ECG, we are developing HeartFailure Explorer and Breast Cancer Explorer which allow for a personalized therapy, resulting in better outcome and more safety and convenience for patients.
Based on the hidden information in the data, the software selects automatically the best combination of methods and allocates the respective software components. Running the parallelized fully automated exploration and optimization process, the software is able to
The available data of the concerned disease serves as the starting point for a new solution. Our proprietary platform detects hidden relationships in any data independent of the realm and provides model-based tests for different diagnostic and therapeutic areas. This approach allows for acquisition of new knowledge about the disease, often resulting in detection of new markers.
While in the classical approach modelers often end up with one model, our software suite generates dozens of models which get validated by the software suite itself. This approach avoids over fitting and leads to more robust models.