Post: Expert: AI can help in the fight against rare cancers

Thanks to AI and health data, diagnosis and treatment of cancer is making progress, according to technology assessors. But regulation is slowing things down.

Save to Pocket listen Print view (Image: greenbutterfly/Shutterstock.com) 4 min. read

By

This article was originally published in German and has been automatically translated.

Thanks to machine learning and, in particular, deep learning using artificial neural networks and the increasing availability of digital health data, the diagnosis and treatment of cancer is making great progress. Biologist Marc Bovenschulte writes this in a short study recently published by the Office of Technology Assessment at the German Bundestag (TAB). According to him, people suffering from rare cancers could also benefit from such approaches based on artificial intelligence (AI). However, such further developments, which involve dealing with increasingly personalized and data-driven medicine, face numerous technical and regulatory challenges.

Rare tumors are defined as those that affect fewer than 6 in 100,000 people. In Germany, these include esophageal, laryngeal and thyroid cancer, Hodgkin’s disease (malignant disease of the lymphatic system) and certain forms of leukemia. According to the study, considering the individual genetic, physical and morphological characteristics of patients increases the chance of "a precisely tailored treatment that is as effective as possible and has as few side effects as possible". AI approaches could be used here in the diagnosis, the selection of suitable therapeutic measures, the prognosis of the course of the disease and the therapeutic support of those affected. The technology also plays a role in the development of medication and new, personalized therapeutic approaches.

AI systems are particularly well suited to evaluating different data such as X-ray images, molecular biological information, sequence information from DNA analyses or literature databases, comparing them with each other, relating them to each other and drawing conclusions from them, explains Bovenschulte. CAD systems, for example, carry out an analysis of the image content in addition to humans and incorporate patterns from comparative or reference data to highlight conspicuous areas. However, this is less successful with small numbers of cases. In a study on the detection of heterogeneous tumors, however, a deep […]

Expert: AI can help in the fight against rare cancers