AI-Driven Decision Support in Oncology: Evaluating Data Readiness for Skin Cancer Treatment

Over the past few years, the field of artificial intelligence (AI) has shown great promise in various domains, including medicine. A potential use case for AI in medicine is its application in managing advanced-stage cancer treatment, where limited evidence often makes treatment choices reliant on the personal expertise of the physicians. The complex nature of oncological disease processes and the multitude of factors that need to be considered when making treatment decisions make it difficult to rely solely on evidence-based trial data, which is often limited and may exclude certain patient populations. This results in physicians making decisions on a case-by-case basis, drawing on their experience of previous cases, which is not always objective and may be limited by the small number of cases they have observed. In this context, the use of clinical decision support systems (CDSS) using similaritybased AI approaches can potentially contribute to better oncology treatment by supporting physicians in the selection of treatment methods [1, 2]. One approach is Case-Based Reasoning (CBR), a subfield of AI that deals with experience-based problem solving.