From data to decision: integrating causality AI and predictive analytics in endourological practice-a descriptive
Chady Ghnatios1,2, Zine-Eddine Khene3,4, Rose Mary Attieh5
1University of North Florida, 1UNF drive, Jacksonville, FL, 32224, USA. Chady.ghnatios@unf.edu.
World Journal of Urology
|March 1, 2026
Summary
Artificial intelligence (AI) is advancing endourology by introducing novel technologies. These innovations aim to improve AI adoption in urology, making its applications more accessible and understandable for clinicians.
Area of Science:
- * Explores the intersection of artificial intelligence (AI) and endourology.
- * Focuses on the integration of advanced AI technologies in urological practice.
Background:
- * Reviews the current state and recent developments of AI and machine learning in urology.
- * Highlights the growing influence of AI across scientific and medical fields.
Purpose of the Study:
- * To provide a guide on AI developments in endourology.
- * To describe the potential integration of advanced AI technologies into clinical practice.
- * To highlight recent advances and novel trends in AI applications for urology.
Main Methods:
- * Reviews state-of-the-art AI and machine learning in urology and endourology.
- * Describes novel AI technologies including causal AI modeling and reinforcement learning.
- * Presents complex AI concepts in a clinician-friendly manner.
Main Results:
- * Showcases potential applications of novel AI technologies to democratize adoption.
- * Explains AI algorithm predictions, adhering to causality and time sequencing.
- * Demonstrates integrating expert knowledge into AI architectures.
Conclusions:
- * Discusses potential applications and limitations of AI in urology.
- * Identifies current challenges impeding the widespread adoption of AI in endourology.
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