SynthesisUrologie (Heidelberg, Germany)2026
[Artificial intelligence as decision support tool in urological oncology: current evidence and challenges].
Synthesis in Urologie (Heidelberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
9 authors.
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Abstract
backgroundThe rising incidence of cancer, increasing life expectancy and complex personalized treatment concepts pose considerable challenges for the healthcare system. Artificial intelligence (AI)-in particular machine learning (ML), deep learning (DL), and large language models (LLMs)-offers promising potential for supporting therapeutic decisions in urological oncology.
objectiveThe aim of this review is to present the current state of research on the application of AI in treatment decisions in urological oncology. For this purpose, a systematic review of publications in the PubMed database was carried out. Methodological approaches, performance indicators and challenges with regard to clinical implementation were analysed comparatively.
resultsDepending on the entity and treatment category, ML models achieve F1-scores between 0.75 and 0.99. Large language models that access external knowledge sources using retrieval-augmented generation (RAG) demonstrate a high degree of guideline compliance, as evidence-based knowledge is specifically integrated into the treatment recommendations. The explainability of the models is mainly ensured by Shapley additive explanation (SHAP) analyses or transparent guideline referencing.
conclusionTo date, the available evidence predominantly consists of proof-of-concept studies, with a particular emphasis on prostate, urothelial, and renal cell carcinoma. Clinical implementation remains limited, in particular due to a lack of prospective validation studies, unresolved data protection and technical challenges, and limited model transparency and explainability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.