Artificial intelligence for diagnosing bladder pathophysiology: An updated review and future prospects.

Area of Science:

• Urology
• Medical Informatics
• Artificial Intelligence

Background:

• Bladder pathophysiology includes diverse conditions such as cancer, infections, and neurogenic bladder.
• Diagnosis and treatment of these conditions present unique challenges.
• Artificial intelligence (AI) offers potential to revolutionize diagnostic methods for bladder disorders.

Purpose of the Study:

• To review the integration of AI in diagnosing bladder pathophysiology.
• To highlight AI techniques (machine learning, deep learning) and their applications.
• To assess current AI tools, their accuracy, clinical utility, and challenges.

Main Methods:

• Comprehensive literature review of AI applications in bladder pathophysiology diagnosis.
• Analysis of machine learning and deep learning techniques.
• Evaluation of AI diagnostic tools, data quality, interpretability, and clinical integration.

Main Results:

• AI, particularly machine learning and deep learning, shows significant potential in identifying and classifying bladder conditions.
• Current AI tools demonstrate varying degrees of accuracy and clinical utility.
• Challenges include data quality, algorithm interpretability, and workflow integration.

Conclusions:

• AI holds considerable promise for enhancing bladder pathophysiology diagnosis.
• Continued progress in data quality, interpretability, and clinical integration is crucial.
• Future AI innovations aim for more accurate, efficient, and personalized patient care.