Artificial Intelligence in Surgery

The Hamlyn Centre for Robotic Surgery, Imperial College London, UK 2. Institute of Medical Robotics, Shanghai Jiao Tong University, ChinaAbstract Artificial Intelligence (AI) is gradually changing the practice of surgery with the advanced technological development of imaging, navigation and robotic intervention. In this article, the recent successful and influential applications of AI in surgery are reviewed from preoperative planning and intra-operative guidance to the integration of surgical robots. We end with summarizing the current state, emerging trends and major challenges in the future development of AI in surgery. Keywords: Artificial intelligence, Surgical autonomy, Medical robotics, Deep learning 1. Introduction Advances in surgery have made a significant impact on the management of both acute and chronic diseases, prolonging life and continuously extending the boundary of survival. These advances are underpinned by continuing technological developments in diagnosis, imaging, and surgical instrumentation. Complex surgical navigation and planning are made possible through the use of both pre-and intra-operative imaging techniques such as ultrasound, Computed Tomography (CT), and Magnetic Resonance Imaging Preprint submitted to Frontiers of Medicine January 6, 2020 arXiv:2001.00627v1 Many terminal illnesses have been transformed into clinically manageable chronic lifelong conditions and increasing surgery is focused on the systematic level impact on patients, avoiding isolated surgical treatment or anatomical alteration, with careful consideration of metabolic, haemodynamic and neurohormonal consequences that can influence the quality of life. For recent advances in medicine, AI has played an important role in clinical decision support since the early years of developing the MYCIN system [5]. AI is now increasingly used for risk stratification, genomics, imaging and diagnosis, precision medicine, and drug discovery. The introduction of AI in surgery is more recent and it has a strong root in imaging and navigation, with early techniques focused on feature detection and computer assisted intervention for both preoperative planning and intra-operative guidance. Over the years, supervised algorithms such as active shape models, atlas based methods and statistical classifiers have been developed [1]. With recent successes of AlexNet [6], deep learning methods, especially Deep Con-volutional Neural Network (DCNN) where multiple convolutional layers are cascaded, have enabled automatically learned data-driven descriptors, rather than ad hoc handcrafted features, to be used for image understanding with improved robustness and generalizability.