A Federated Learning Approach to Real-Time AI-Assisted Laparoscopic Surgery with Privacy Preservation

Abstract

Federated learning (FL) presents a transformative approach to enhancing real-time artificial intelligence (AI) capabilities in laparoscopic surgery while upholding stringent privacy standards. This research introduces an innovative FL framework tailored for laparoscopic procedures that enables collaborative machine learning without direct data exchange. The cornerstone of this model is its ability to learn from decentralized data sources—individual healthcare facilities retaining their data locally—thus circumventing traditional privacy concerns associated with centralized data storage. The proposed system integrates real-time AI analytics to assist surgeons by providing enhanced visualizations and predictive analytics during procedures, leveraging data from a consortium of participating hospitals. Each node in the federated network trains an algorithm locally, and only model updates are shared across the network, ensuring that sensitive patient data remains within the hospital’s firewall. This method not only preserves privacy but also allows for the incorporation of vast, diverse datasets that improve the robustness and accuracy of the AI models. Our evaluation demonstrates significant improvements in surgical outcomes, including reduced operation times and enhanced accuracy of procedural tasks, validated through a series of controlled trials across multiple sites. The federated learning model effectively adapts to the unique workflows of different surgeons and operating environments, showcasing its potential for widespread adoption in the medical field. This approach addresses critical challenges in deploying AI in healthcare settings, particularly the need for privacy-preserving techniques that comply with regulatory frameworks while harnessing the power of collective data intelligence to advance medical technology and patient care.