Artificial intelligence and machine learning in oncologic imaging

Background. Machine learning (ML) is finding entry into many areas of society, including medicine. This transformation has the potential to drastically change the perception of medicine and medical practice. While these advances currently only influence clinical routine in isolated cases, they also come with risks. These aspects become particularly clear when considering the different stages of oncologic patient care and the involved interdisciplinary and intermodality interactions. In recent publications, computers-in collaboration with humans or alone-have been outperforming humans. This pertains to tumor identification, tumor classification, creation of prognoses, and evaluation of treatments. Additionally, ML algorithms, e.g., artificial neural networks (ANNs), which constitute the drivers behind many of the latest achievements in ML, can deliver this level of performance in a reproducible, fast, and cheap manner. Objective. This review elucidates the current state of research on ML in oncology by focusing on selected tumor entities, and relates this to the development of research and medicine as a whole. Materials and methods. This work is based on a selective literature search in the databases PubMed and arXiv. Conclusion. In the future, AI applications will develop into an integral part of the medical profession and offer advantages for oncologic diagnostics and treatment.