Research on Artificial Intelligence to Support Diagnosis and Therapy
Today, most of the information in clinics and medical practices is stored digitally. Until now image data, findings, lab values, digital patient records, and surgery reports are handled separately. However, there is a current trend aimed at gathering this information in one unified software framework.
All relevant information in one central system
This data integration enables faster handling of medical information and lays the foundation for more efficient interaction between different specialties and to enable more precise and personalized clinical decisions. It also promises added value: New self-learning computer algorithms can detect hidden patterns in the data and give physicians valuable support for their diagnosis and therapy decisions.
Research develops decision support systems based on deep machine learning
With their joint research alliance, Siemens Healthineers and the Fraunhofer Institute for Medical Image Computing MEVIS will support physicians in finding the right course of therapy for their patients. Both partners are jointly developing artificial intelligence software systems to facilitate diagnosis and therapy decisions with the help of advanced data integration, comprehensive databases, and automatic recognition of patterns and regularities in data (deep machine learning). The goal is to support physicians to define the best possible treatment approach for their patients fast and ensure that they receive the maximum benefit with minimum side effects.
Focus on tumor diseases
Based on comprehensive databases, the research partners will develop software systems that support clinicians in finding the best possible course of therapy. The work focuses on tumor diseases, such as lung cancer, for which physicians have to determine the necessity of a biopsy, a procedure known to be stressful for patients. The systems of Fraunhofer MEVIS and Siemens Healthineers would support physicians’ decisions in the future. The goal is to let the software display all the information that could be relevant for decision-making. A physician would not have to gather information from separate sources like X-ray and MR images, tissue analyses, genetic parameters, lab values, and important data from the patient’s medical history, saving valuable time. Additionally, the guidelines of medical specialist societies will be integrated automatically, providing physicians with valuable support. Ultimately, the algorithms will link the case at hand with a comprehensive database.
The partners already have elementary access to necessary reference databases, but much will be developed and completed after the project commences.
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The statements by Siemens’ customers described herein are based on results that were achieved in the customer's unique setting. Since there is no "typical" hospital and many variables exist (e.g., hospital size, case mix, level of IT adoption) there can be no guarantee that other customers will achieve the same results.