Transforming abdominal reconstruction-impact of artificial intelligence algorithms and advanced technologies on the efficiency of vascularized flaps and postoperative functional restoration: a systematic review

Abdominal Reconstruction shows the progress created by artificial intelligence and machine learning AI & ML, especially those involving vascularized flaps. Therefore, this systematic review seeks to find out how incorporating AI can transform surgical accuracy, minimize post-surgical complications, as well as improve the recovery process. AI is already being used for planning surgery forecasting failure of flaps as well and minimizing SSI. Machine learning models like neural networks demonstrate impressive accuracy in identifying high-risk patients such as those with obesity, chemotherapy exposure, or large fascial defects. Real-time data analytics, remote monitoring through AI and ML have improved the decision-making process and led to efficient surgeries and better functional outcomes by reducing surgical failure and post-operative complications. Integrating AI into complex surgical environments requires carefully balancing machine recommendations and human expertise yet ethical concerns surrounding data transparency, bias, and patient privacy and these concerns need critical consideration and must be addressed. We conducted this review systematically to evaluate existing studies, revealing that while AI is promising to improve surgical outcomes, its real-world applications are still in their infancy, and we will evaluate how AI has transformed abdominal reconstruction surgical procedures, plastic surgeries, such as breast reconstruction or abdominal wall hernias, or other oncological resections.