AI As a Multi-faceted Instrument in Healthcare

The idea has been around for decades, yet few people know what Artificial Intelligence technology is really capable of. Some argue that it has the power to transform care delivery and cultivate precision medicine. And just maybe, it’s already begun to do so…



Machines are getting smarter. This is good news for the healthcare sector. Especially for radiologists, faced with shrinking workforces and rapidly increasing workload, this could provide answers.

After the 1996 checkmate of Deep Blue against Garry Kasparov,  the 2011 machine vs. man victory on Jeopardy, and one of the world’s best Go players admitting defeat in 2016, AI has finally matured enough to take on clinical questions. Broader applications, made possible by the exponential growth of computing power, are offering manufacturers like Siemens Healthineers opportunities to develop solutions that will help their customers transform their field.

Medical imaging of course plays a major role in the rapidly evolving realm of Artificial Intelligence (AI), but the disruption will go much further: According to The Economist, 54% of healthcare leaders believe that in a mere 5 years, AI’s role in medical decision support will expand considerably. For the long term, 74?lieve that big data and AI are major drivers in the identification of medical approaches based on genetic, environmental and lifestyle factors.*

“Radiology is set to transition from just an image-based specialty to something that integrates more and more information into the diagnostic process and into the process of guiding treatment and treatment decision support. So the way we see it, radiology has the chance to become the information integration and interpretation platform within medicine,” says Walter Maerzendorfer, President Diagnostic Imaging, Siemens Healthineers.


AI is a computer-aided process for solving complex problems, like pattern recognition, speech recognition, and knowledge-based decision support. While classic algorithms follow fixed paths laid out by the programmer, machine learning algorithms work out the way to the solution independently, based on exemplary data – whereas “Deep Learning” means constantly optimizing algorithms with high volumes of data, leading to even better results than machine learning. Siemens Healthineers have been involved in the field since the 1990s, which led to more than 400 patents in  Machine Learning, 75 basic patents in “Deep Learning” and more than 30 AI-powered applications.

Unlike the AI pioneers in the 1980s and 1990s, today we have sufficient volumes of training data and the computing capacities to allow the implementation of deep neuronal networks. High quality data is the fuel for continuously improving results. Therefore, Siemens Healthineers has invested in a dedicated advanced reading and annotation team, building a database of more than 100 million curated images, reports, clinical, and operational data to train their algorithms on. This database serves as the backbone of the fast-growing Siemens Healthineers portfolio of products and services with built-in AI.


The purpose of AI is never a competition “man vs machine” but collaboration. In the clinical workflow, there are five main areas where “man + machine” can achieve tangible clinical improvements: The first one is the examination phase, where intelligent and automated scan procedures can support the image acquisition. Secondly, in the detection phase, measurements, segmentation, and land marking, complemented with AI, can play a major role. Thirdly, in the characterization phase, AI helps finding abnormalities, then comparing it to the normal population. Next, AI can assist in fully computer-acquired diagnoses, findings, and also disease biomarkers. And last but not least, AI is transforming the therapy decision support phase, listing and ranking treatment options to support faster, more confident decisions.


In the last two years, Siemens Healthineers has completed extensive strategic research to draw the picture of the future in radiology: From analysis of mega-trends in the market to technology trends and deep machine learning, big data, and data analytics, technology with built-in AI is proving to be transformational in the healthcare market.

“Our R&D activities are being infused with AI technology, creating new opportunities. For example, on the population health management level, where the episodes of single patients are accumulated in a collective data pool, even beyond the country level. And these data pools can be used to tap into with data analytics to learn what works in healthcare, and what doesn’t, and draw conclusions for the right standards of care for the individual patients.”, says Siemens Healthineers Diagnostic Imaging Head Walter Maerzendorfer.

Ultimately, the healthcare industry seems to be well on the way when it comes to AI. Translating the learnings from these big data pools into tailor-made diagnostics, then designing treatment approaches for the individual patient by drawing on data-driven precision medicine.

That’s what it’s all about.

More on Artificial Intelligence at Siemens Healthineers:

* The future of healthcare 22 November 2017, The Economist

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