OBJECTIVE: We used machine learning to develop and validate a multivariable algorithm allowing the accurate and early prediction of postoperative hypocalcemia risk.
We have assessed the chatbot Generative Pretrained Transformer, a type of artificial intelligence software designed to simulate conversations with human users, in an experiment designed to test its relevance to scientific writing. chatbot Generative ...
OBJECTIVE: To develop and externally validate an updated artificial intelligence (AI) prediction system for stratifying the risk of lymph node metastasis (LNM) in T2 colorectal cancer (CRC).
OBJECTIVE: To examine the use of surgical intelligence for automatically monitoring critical view of safety (CVS) in laparoscopic cholecystectomy (LC) in a real-world quality initiative.
OBJECTIVE: The aim of this study was to develop and test a prototype of a deep learning surgical guidance system [computer-assisted staging laparoscopy (CASL)] that can intraoperative identify peritoneal surface metastases on routine laparoscopy imag...
OBJECTIVE: To develop an artificial intelligence (AI) system for the early prediction of residual cancer burden (RCB) scores during neoadjuvant chemotherapy (NAC) in breast cancer.
OBJECTIVE: To evaluate whether a machine-learning algorithm (ie, the "NightSignal" algorithm) can be used for the detection of postoperative complications before symptom onset after cardiothoracic surgery.
OBJECTIVE: The aim of this study was to develop a novel machine learning model to predict clinically relevant postoperative pancreatic fistula (CR-POPF) following pancreaticoduodenectomy (PD).
OBJECTIVE: To develop and validate TraumaICDBERT, a natural language processing algorithm to predict injury International Classification of Diseases, 10th edition (ICD-10) diagnosis codes from trauma tertiary survey notes.