ARTIFICIAL INTELLIGENCE–DRIVEN DECISION SUPPORT IN RENAL SURGERY: IMPROVING PRECISION AND PERIOPERATIVE OUTCOMES IN KIDNEY CARE

Abstract

Background: Renal surgery involves a delicate decision making process to strike a balance between an oncological control and maintenance of renal functions. Artificial intelligence (AI) has become an attractive resource to augment clinical decision support along the perioperative continuum.

Objective: This paper assesses the importance of AI-driven decision-support systems in enhancing surgical precision and perioperative outcomes in renal surgery.

Methods: Clinical, imaging and perioperative data on patients undergoing renal surgery were used to conduct a retrospective analysis. Deep learning and machine learning models were designed to forecast the complexity of surgery, the possibility of complication and the postoperative outcomes. The standard evaluation metrics were used to evaluate model performance and compare it to the traditional methods.

Results: AI models showed high predictive accuracy with multiple endpoints, and significant improvements in surgical accuracy, including higher negative margin rates and increased nephron preservation. The AI-assisted cases also improved the perioperative outcomes, including the operative time, blood loss, and complication rates.

Conclusion: The use of AI in decision support improves surgical planning, execution, and prediction of outcomes in renal surgery. Its incorporation into clinical workflows can enhance the degree of accuracy, minimize complications, and develop individualized kidney care.