p<.0001), Bokhman type 2 versus type 1 (HR 1.55 [1.04-2.31], pZ0.03), grade 3 versus 1 (HR 2.57 [1.26-5.24], pZ0.009), and concurrent versus sequential treatment (HR 1.56 [1.09-2.22], pZ0.01) were the strongest predictors of worse OS. Conclusion: This large population-based study suggests that about twothirds of patients with node-positive endometrial carcinoma could be cured with adjuvant multimodality treatment. Upfront CT followed by RT may be a better treatment sequence in this population of patients. Prospective studies addressing this question are warranted.

Artificial intelligence (AI) demonstrated by machines is based on reinforcement learning and revolves around the usage of algorithms. The purpose of this review was to summarize concepts, the scope, applications, and limitations in major gastrointestinal surgery. This is a narrative review of the available literature on the key capabilities of AI to help anesthesiologists, surgeons, and other physicians to understand and critically evaluate ongoing and new AI applications in perioperative management. AI uses available databases called “big data” to formulate an algorithm. Analysis of other data based on these algorithms can help in early diagnosis, accurate risk assessment, intraoperative management, automated drug delivery, predicting anesthesia and surgical complications and postoperative outcomes and can thus lead to effective perioperative management as well as to reduce the cost of treatment. Perioperative physicians, anesthesiologists, and surgeons are well-positioned to help integrate AI into modern surgical practice. We all need to partner and collaborate with data scientists to collect and analyze data across all phases of perioperative care to provide clinical scenarios and context. Careful implementation and use of AI along with real-time human interpretation will revolutionize perioperative care, and is the way forward in future perioperative management of major surgery.