For centuries, patients have sought medical help for their ailments.
Just as in the past, however, there are still many illnesses – both wellknown, widespread diseases and rare conditions – that initially cause few or inconclusive symptoms, and many patients leave the doctor’s office with an incorrect diagnosis. In addition, diseases may progress slowly or quickly depending on the individual. Sometimes it is best to “watch and wait”, while in other cases patients should receive intensive treatment from an early stage. In the context of ever-increasing scientific knowledge, such complex medical decisions present physicians with great challenges. We are inundated with digital information. As digitization of healthcare systems progresses, new and previously unimagined opportunities to improve patient care come to light.
For example, machine learning and artificial intelligence technologies are making it possible to elicit important information from routinely collected health data: Who is most likely suffering from a certain disease but remains undiagnosed? Which patient is at a high risk of progression – and which patients are most likely to benefit from a new treatment?
In this white paper, we present practical case studies for artificial intelligence applications in medical care. We describe important methodological approaches for the development of what are known as predictive algorithms and show ways in which these algorithms are already being employed successfully in healthcare practice today. We also discuss the future outlook for the widespread use of predictive algorithms in medical practice as well as potential obstacles that may impede it.
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