How do cancer cells differ from healthy cells? A new machine learning algorithm called "ikarus" knows the answer, reports a team led by MDC bioinformatician Altuna Akalin in the journal Genome Biology. The AI program has found a gene signature characteristic of tumors. When it comes to identifying patterns in mountains of data, human beings are no match for artificial intelligence (AI). In particular, a branch of AI called machine learning is often used to find regularities in data sets – be it for stock market analysis, image and speech recognition, or the classification of cells.

How do cancer cells differ from healthy cells? A new machine learning algorithm called "ikarus" knows the answer, reports a team led by MDC bioinformatician Altuna Akalin in the journal Genome Biology. The AI program has found a gene signature characteristic of tumors. When it comes to identifying patterns in mountains of data, human beings are no match for artificial intelligence (AI). In particular, a branch of AI called machine learning is often used to find regularities in data sets – be it for stock market analysis, image and speech recognition, or the classification of cells.

The advent of new field strengths of up to 10.5 Tesla allows magnetic resonance imaging in unprecedented detail. This opens up enormous opportunities in cardiac, neurological, and experimental medicine. Researchers will discuss these new possibilities at the MDC's Ultrahigh Field Magnetic Resonance Symposium on September 2 and 3. The use of magnetic resonance imaging (MRI) at 1.5 Tesla has long been a standard part of clinical practice. And about one in five major hospitals already has a 3-Tesla machine.

AI has embraced medical applications from its inception, and some of the earliest work in successful application of AI technology occurred in medical contexts. Medicine in the twenty-first century will be very different than medicine in the late twentieth century. Fortunately, the technical challenges to AI that emerge are similar, and the prospects for success are high.

AI has embraced medical applications from its inception, and some of the earliest work in successful application of AI technology occurred in medical contexts. Medicine in the twenty-first century will be very different than medicine in the late twentieth century. Fortunately, the technical challenges to AI that emerge are similar, and the prospects for success are high.