Artificial intelligence (AI) is transforming care delivery and expanding precision medicine. Siemens Healthineers has served as a pioneer in AI development for more than 20 years, and new deep learning technology now enables us to automate complex diagnostics and support optimal treatment.
Our Al-powered solutions address major challenges that the healthcare field faces. Right now, the demand for diagnostic services outstrips the supply of experts in the workforce. Developing solutions for managing this ever-increasing workload is a crucial task for the healthcare sector. And, while the workload is growing, diagnostics and treatment are also becoming more complex. Diagnostic experts and physicians need a new set of tools that can handle large volumes of medical data quickly and accurately. This would allow for more objective treatment decisions based on quantitative data and tailored to the needs of every patient. To provide this new toolset, we need to draw on the power of AI.
Siemens Healthineers has developed a portfolio of AI solutions that help automate and standardize complex diagnostics to meet the needs of every patient. With established AI expertise, future-oriented staff, vast medical data sets, and the exceptional computing power needed for creating algorithm-supported healthcare solutions, we are the right partner for venturing into the world of AI.
See AI healthcare technology in action
Watch this video from RSNA 2017 in Chicago to a selection of our AI technology in action and listen to our experts explain the key challenges in healthcare today.
Deep learning algorithms explained
Watch this video from Arab Health 2018 to learn how deep learning algorithms can simplify, and enhance the accuracy of, certain medical procedures.
Workload and less interpretation time – daily problems of radiologists today
More and more exams need to be evaluated. But who will do it?
In many countries, the number of CT and MRI exams explodes, but the number of experts does not grow proportionately. As a result, the workload per radiologist increases dramatically. 100 studies per day and 12+ hour workdays are not unusual.
With shorter turnaround time, the error rate rises.
The retrospective error rate among radiological exams is 30%.1 Studies show that cognitive factors significantly contribute to diagnostic errors.2 Cutting in half the interpretation time of radiologists increases the interpretation error rate percentage by 16.6%.3
1Berlin L (2007): Radiologic Errors and Malpractice: A Blurry Distinction.
2Lee C, Nagy PG, Weaver SJ and Newman-Toker DE: Cognitive and System Factors Contributing to Diagnostic Errors in Radiology.
3Berlin L: Faster Reporting Speed and Interpretation Errors: Conjecture, Evidence, and Malpractice Implications.