ANALYSIS OF THE EFFECT OF ARTIFICIAL INTELLIGENCE-ASSISTED MINIMALLY INVASIVE TREATMENT FOR URINARY CALCULI

Urinary calculi, a prevalent urinary system disorder, significantly impairs patients’ quality of life and exhibits an escalating incidence. While minimally invasive surgery offers clinical advantages, it is challenged by complications, high costs, and inconsistent standards. This study explores the role of artificial intelligence (AI) in enhancing minimally invasive treatment for urinary calculi, addressing unmet needs in precision and efficacy.Using a systematic analysis, the research examines AI applications across the treatment continuum: preoperatively, AI predicts stone composition, evaluates size/location, and optimizes surgical strategies through data-driven models; intraoperatively, it enhances procedural safety and outcomes via real-time decision support; postoperatively, AI aids risk assessment for recurrence and guides personalized follow-up to reduce complications. Findings reveal that AI integration improves treatment customization and precision by synthesizing multi-dimensional clinical data, yet challenges persist, including model accuracy limitations, standardization gaps, variable physician proficiency, and economic barriers.Innovatively, this study highlights AI’s potential to transform holistic management of urinary calculi while identifying critical implementation hurdles. It underscores the need for technological refinement, standardized protocols, clinician training, and cost-containment measures to facilitate widespread adoption. By bridging AI capabilities with clinical practice, this analysis provides a practical framework for advancing minimally invasive therapies, ultimately aiming to enhance patient care through evidence-based, AI-driven solutions.