Artificial Intelligence in Surgery

Abstract

Recent advances in medicine have made it possible to employ artificial intelligence (AI) to aid clinical decision-making, with applications ranging from risk assessment, to genomics and to imaging; and from diagnostics to precision medicine and to drug development [1,2]. Surgeons have recently begun to use AI in their practices, with emphasis on imaging, navigation and early approaches by concentrating on feature identification. In the preoperative planning phase, doctors use patient's medical information and imagings to devise a surgical strategy. Anatomical classification, detection segmentation, and picture registration are all being aided at this step by deep learning, which utilizes image analysis methods and conventional machine learning for classification. The core of minimally invasive surgery (MIS) has always been computer-aided intraoperative guidance. Using a computer vision technique known as radiomics, Swiss researchers have developed methods in rapidly diagnosing COVID-19 patients using CT scans. This discovery was made in less than a year after the pandemic and it demonstrated the critical role artificial intelligence can play in healthcare and public health. By interpreting enormous volumes of imaging data, radiology employs imaging and algorithms to improve diagnostic accuracy. Oncology is where AI is most often used. Adjuvant chemotherapy for early-stage non-small cell lung cancer can be predicted using a radiomics risk score and an accompanying nomogram based on the results of recent studies.