Artificial intelligence and machine learning in neurogenic lower urinary tract dysfunction and spinal cord injury: current status, emerging technologies, and ethical perspectives.

Artificial intelligence (AI) and machine learning are increasingly applied across urology, with particular promise for the management of neurogenic lower urinary tract dysfunction (NLUTD) in individuals with spinal cord injury (SCI). There is also particular promise in the management of NLUTD, a complex, lifelong condition affecting individuals with SCI and also occurring in the context of multiple sclerosis, Parkinson's disease, and other neurological disorders. This article provides a comprehensive analysis of AI's current and emerging role in neuro-urology, with dedicated focus on the specific challenges and opportunities presented by NLUTD and SCI. We review the epidemiological and clinical burden of neurogenic bladder dysfunction. We further examine AI applications in uro-oncology, functional urology, digital pathology, and robotic surgery, integrating these with the specialized perspective of neuro-urology. The particular vulnerability of individuals with SCI to algorithmic bias, access inequity, and assistive technology dependency is critically discussed. We identified five key domains where AI may transform care for patients with NLUTD: automated urodynamic interpretation including detection of detrusor overactivity and detrusor-sphincter dyssynergia, predictive risk stratification for renal deterioration, closed-loop neuromodulation, remote digital monitoring, and AI-assisted rehabilitation support. Alongside the substantial opportunities, we articulate the ethical, moral, and societal responsibilities that accompany AI integration in this population, emphasizing that patients with SCI and NLUTD deserve not merely inclusion in AI development but active partnership as co-designers of the technologies that will shape their lives.