Journal of gastroenterology and hepatology
-
J. Gastroenterol. Hepatol. · Feb 2021
ReviewApplications of artificial intelligence in pancreatic and biliary diseases.
The application of artificial intelligence (AI) in medicine has increased rapidly with respect to tasks including disease detection/diagnosis, risk stratification, and prognosis prediction. With recent advances in computing power and algorithms, AI has shown promise in taking advantage of vast electronic health data and imaging studies to supplement clinicians. Machine learning and deep learning are the most widely used AI methodologies for medical research and have been applied in pancreatobiliary diseases for which diagnosis and treatment selection are often complicated and require joint consideration of data from multiple sources. The aim of this review is to provide a concise introduction of the major AI methodologies and the current landscape of AI research in pancreatobiliary diseases.
-
J. Gastroenterol. Hepatol. · Feb 2021
Randomized Controlled Trial Comparative StudyRemimazolam tosilate in upper gastrointestinal endoscopy: A multicenter, randomized, non-inferiority, phase III trial.
Remimazolam tosilate (RT) is a new short-acting GABA(A) receptor agonist, having potential to be an effective option for procedural sedation. Here, we aimed to compare the efficacy and safety of RT with propofol in patients undergoing upper gastrointestinal endoscopy. ⋯ This trial established non-inferior sedation success rate of RT compared with propofol. RT allows faster recovery from sedation compared with propofol. The safety profile is favorable and appears to be superior to propofol, indicating that it was feasible and well tolerated for patients.
-
J. Gastroenterol. Hepatol. · Feb 2021
Challenges of developing artificial intelligence-assisted tools for clinical medicine.
Machine learning, a subset of artificial intelligence (AI), is a set of computational tools that can be used to enhance provision of clinical care in all areas of medicine. Gastroenterology and hepatology utilize multiple sources of information, including visual findings on endoscopy, radiologic imaging, manometric testing, genomes, proteomes, and metabolomes. However, clinical care is complex and requires a thoughtful approach to best deploy AI tools to improve quality of care and bring value to patients and providers. ⋯ There is still much work to be done on a broader societal level in developing ethical, regulatory, and reimbursement frameworks. A multidisciplinary approach and awareness of AI tools will create a vibrant ecosystem for using AI-assisted tools to guide and enhance clinical practice. From optically enhanced endoscopy to clinical decision support for risk stratification, AI tools will potentially transform our practice by leveraging massive amounts of data to personalize care to the right patient, in the right amount, at the right time.