Artificial intelligence in surgical practice: Truth beyond fancy covering

Abstract

In recent years, the role of artificial intelligence (AI) in surgical applications has been increasing, leading to significant changes in the healthcare field.AI is used in areas such as supporting surgeons' decision-making processes, evaluating surgical skills, and improving training processes.However, literature reviews on the applicability and effectiveness of this technology also show that integrating AI into surgical practice has some challenges.When considering the application areas of AI in surgery, the main headings are as follows.In the preoperative period, diagnosis, clinical risk prediction, and selection of suitable patients for surgery, evaluation of the patient's preoperative data, identification and intervention of concomitant conditions that can be optimized, informing the patient about the surgery, presentation of appropriate written and visual materials, and AI contributions to the patient's education and consent process can improve the results.Intraoperatively, identifying surgical instruments and the stage of the operation and predicting procedural next steps may accelerate surgical decision-making and provide recommendations regarding possible outcomes.Additionally, perhaps after the development and integration of the operating room black box, one can envisage the operating theater of the future with access to dashboards updated with real-time data specific to the patient and surgical team.Developments in the fields of surgical robotics and automation are becoming increasingly important.The evaluation of intraoperatively obtained data streams and autonomous systems can be prepared based on these.The objective criteria and feedback produced through AI can improve the field of surgical training, and dynamic simulations can offer more realistic surgical training opportunities.During the postoperative period, monitoring patients via wearable device technology and sensors can improve many parameters, such as early warning, mobilization, and discharge.Prediction of complications can enable follow-up recovery.With the inclusion of mobile technologies, innovations toward the goal of remote monitoring are increasing and allow for a home-based recovery model (1,2).Visual AI applications have also attracted attention as a part of surgical practice.In one study, an algorithm for predicting unwanted bleeding caused by surgical instruments during robotic and laparoscopic surgery was developed.This algorithm detects sudden movements of surgical instruments and predicts the possibility of