Artificial Intelligence and Urinary Tract Infections: A Diagnostic Perspective

Infections of the urinary system (UTIs) are common and triggered by bacteria including Klebsiella pneumonia, E. coli as well as Pseudomonas aeruginosa. These infection can affect the bladder, urethra, and kidneys, leading to symptoms such as dysuria, urgency, and frequency changes. Clinical symptoms and urine cultures are used to diagnose, but both have limitations in terms of speed and accuracy. Artificial intellect (AI) has emerged as the promising tool to enhance UTIs diagnosis and treatment. AI techniques, such as machine learning techniques including Support Vector Machines, Random Forests, and Artificial Neural Networks, have demonstrated high accuracy in pathogen detection and antibiotic resistance prediction, reducing diagnostic workload and errors. Based on investigations, AI has the ability to improve diagnostic accuracy and efficiency, thereby positively impacting patient outcomes. The integration of intelligent technology in UTI diagnostics promises advances in rapid, accurate, and individualized patient care that tackles antimicrobial resistance and diagnostic delays.