Artificial Intelligence Machine Based Learning Application and Large Database Analyses in Care of Renal Cell Carcinoma Patients

Generative Artificial Intelligence (AI) and Machine based learning platforms as well as analyses of large databases represent a fast-moving area of innovation in healthcare. The incidence of Renal Cell Carcinoma (RCC) is increasing approximately 2-3% per year, underscoring the need for individualized approaches to care. Improvements in diagnosis, risk stratification, and treatment selection is crucial for achieving precision care in RCC. Application of Machine Based Learning and Artificial Intelligence as well as understanding large datasets to the management of RCC patients holds great promise for advancing precision care. However, it remains associated with ongoing controversy and practical limitations.The management of RCC remains complex and evolving. Challenges include increasing global incidence, significant tumor heterogeneity, and limitations in early diagnosis, risk stratification, and prediction of treatment response. Existing clinical models often fail to capture the full extent of disease heterogeneity, leading to oversimplified, one-size-fits-all management approaches that may adversely affect patient outcomes. Novel advances such as AI and Machine Based Learning Platforms are transforming the landscape of cancer care. This thematic issue aims to explore the current state and limitations as well as future directions in RCC management, as these advances may enhance clinical decision-making and optimize patient outcomes. We welcome Original Research, Review, Mini Review and Perspective articles on themes including, but not limited to:This thematic issue aims to explore different aspects of utilization of machine based learning and artificial intelligence in Renal Cell Carcinoma. In particular, the following areas will be explored:1. Optimalization and optimization of risk stratification and personalized medicine 2. Utilization of and impact on patient care models.3. Patient perception, ethical considerations, and limitations of machine based learning and generative AI. Topic editors: Ithaar H Derweesh, Michael Staehler Source: https://www.frontiersin.org/research-topics/80248/artificial-intelligence-machine-based-learning-application-and-large-database-analyses-in-care-of-renal-cell-carcinoma-patients