Surgical complications pose significant challenges for surgeons, patients, and health care systems as they may result in patient distress, suboptimal outcomes, and higher health care costs. Artificial intelligence (AI)-driven models have revolutioniz...
The liver is the most common place for colon adenocarcinoma metastasis because of portal circulation. The surgical intervention for patients with colon adenocarcinoma with synchronous metastasis to the liver has been debated. Studies have shown that ...
INTRODUCTION: Minimally invasive esophagectomy (MIE) has not been associated with a long-term survival advantage compared to open esophagectomy (OE). We investigated survival differences between MIE, including laparoscopic and robotic, and OE.
The majority of retroperitoneal mass excision is performed via conventional "open" laparotomy due to concerns of technical difficulty and adequate oncological margins in cases of a malignant sarcoma. A very few cases of minimally invasive resection b...
Interest in the use of artificial intelligence (AI) and machine learning (ML) in medicine has grown exponentially over the last few years. With its ability to enhance speed, precision, and efficiency, AI has immense potential, especially in the field...
In 2018, general surgery topped the number of robotic cases. Over 90% of residents participate, but only 65% of programs have a formal curriculum, and less than half track progress. Many are insufficient at training due to an observational role. This...
BACKGROUND: Minimally invasive liver resection is gradually becoming the preferred technique to treat liver tumors due its salutary benefits when compared with traditional "open" method. While robotic technology improves surgeon dexterity to better p...
We aimed to assess whether early exposure of medical students to robotic surgery training influences their interest in a surgical career and improves scores on objective simulation tasks. Medical students were invited to participate in robotic online...
BACKGROUND: There is a significant mortality burden associated with emergency general surgery (EGS) procedures. The objective of this study was to develop and validate the use of a machine learning approach to predict mortality following EGS.