Sondersituation der Daten in der Onkologie

Translated title of the contribution: Data complexity in oncology

P. Metzger, L. Gräßel, A. L. Illert, M. Boerries

Research output: Contribution to journalReview articlepeer-review


The use of artificial intelligence (AI) in oncology promises to continuously improve cancer treatment and prevention. In addition to improving diagnosis and optimizing therapy, AI is also helping to increase the efficiency of clinical processes. The automation of routine tasks and AI-based decision-support systems facilitate physicians’ work by providing relevant information in real time. In addition, AI algorithms are demonstrating impressive capabilities in the precise detection of tumors through the analysis of medical image data and the identification of genetic markers for personalized therapy approaches. A prerequisite for this is the provision of data, which is also a challenge in oncology. The complexity of the different types of data, including medical images, genomic data, and clinical information, requires not only advanced analysis methods, but also the provision of data in a standardized form. In addition, privacy and ethical issues must be addressed and considered when using sensitive patient data. Transparency and interpretability of AI algorithms are critical to building trust in the technology. The combination of AI and oncology signals a paradigm shift towards more precise, personalized, and efficient patient care, while improving quality of life. While the positive impact on diagnostic accuracy and treatment optimization is promising, overcoming the data-related challenges requires continuous collaboration between researchers, physicians, and patients.

Translated title of the contributionData complexity in oncology
Original languageGerman
Pages (from-to)347-352
Number of pages6
Issue number5
StatePublished - May 2024


Dive into the research topics of 'Data complexity in oncology'. Together they form a unique fingerprint.

Cite this