About this Research Topic
Artificial Intelligence (AI) has found numerous applications across the fields of Science, including medicine. However, issues such as data rarity and noise introduction make medicine a challenging context for AI. Urology inherits the complications of medicine and raises additional concerns, such as tiny Regions of Interest (ROI) in the example of prostate tumor segmentation in medical imaging. While application of AI to Urology has been researched and successful models do exist, the true potential of AI in the field is unexplored. AI is a fast-growing field with multiple novel algorithms being emerged each year, and importing new ideas from AI to Urology demands more research.
Artificial Intelligence in Urology aims to foster applications of recent AI algorithms to the field. This Research Topic therefore welcomes reviews and research studies with academic impact in the field. We encourage reproducible research and thus prioritize submissions with available data and code, and robust methodology. As the main element of reproducible applied AI research, we appreciate measurements of randomness. Additionally, we embrace contributions to prevalent challenges of AI such as training models on imbalanced datasets in classification scenarios. We consider clinical endpoints an important element for AI application, such as prognosis, treatment response, tumor classification and subtyping. Nonetheless, submissions will not be limited to these sub-themes, and we would support any contribution which is impactful to the field within both adult and pediatric urology.
Artificial Intelligence in Urology aims to foster applications of recent AI algorithms to the field. This Research Topic therefore welcomes reviews and research studies with academic impact in the field. We encourage reproducible research and thus prioritize submissions with available data and code, and robust methodology. As the main element of reproducible applied AI research, we appreciate measurements of randomness. Additionally, we embrace contributions to prevalent challenges of AI such as training models on imbalanced datasets in classification scenarios. We consider clinical endpoints an important element for AI application, such as prognosis, treatment response, tumor classification and subtyping. Nonetheless, submissions will not be limited to these sub-themes, and we would support any contribution which is impactful to the field within both adult and pediatric urology.
Keywords: Urology, Artificial Intelligence, Machine Learning, Deep Learning
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
