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Background and Aims: Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis. Methods: The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board. Results: Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett’s esophagus, and detection of various abnormalities in wireless capsule endoscopy images. Conclusions: The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.

The article discusses research being done on the use of a gastric balloon in obesity surgery referencing the study "The first Procedureless Gastric Balloon for Weight Loss: A Prospective, Observational, Open-Label, Multicenter Study," by E. Machytka et al. in the 2017 issue.

Purpose/objectives: Despite increased emphases on reducing racial disparities in the U.S. health care system, interprofessional care teams may inadvertently perpetuate health disparities through lack of awareness or experience in supporting individualized, patient-centered goals of care. Racial disparities can lead to health inequity. Persistent health disparity gaps exist among Black patients with multiple myeloma (MM) when compared with non-Black patients. Black patients experience a two-fold increase in MM risk and earlier age of onset compared with non-Black patients. Black patients are also less likely to receive timely access to some therapies, undergo autologous stem cell transplant, or enroll in clinical trials. This article describes a large-scale, equity-focused implementation science initiative aimed at identifying and overcoming racial disparities and health inequity among patients with MM through quality improvement goals identified by each of the interprofessional cancer care teams., Primary Practice Settings: Interprofessional cancer care teams in two large oncology systems as well as four community clinics were engaged in this study along with their patients with MM. Geographic areas included the following: Chicago, IL; Washington, DC; Charlotte, NC; Columbus, OH; Denver, CO; and Indianapolis, IN. Interprofessional teams included hematologists/oncologists, primary care physicians, nurse practitioners/physician assistants, and case managers/nurse navigators. Teams collectively examined and compared their own beliefs and attitudes about their patients' goals for MM treatment and management versus those of their patients to uncover and address discordances. Medical records from the clinics were audited to evaluate disparities in treatment and practice at the point of care. Live, team-based audit-feedback sessions were implemented among teams to examine data sets, as well as utilize the data to address interprofessional factors that could enhance more equitable care., Findings/conclusions: Data from comparative surveys between patients and interprofessional team members revealed significant discordances that enabled health care teams to recognize gaps and identify ways to improve patient-centered care, such as shared decision-making. Through audit-feedback sessions, interprofessional teams were able to collaboratively meet and discuss methods to improve access to care coordination services and other strategies aimed at alleviating disparities. Baseline chart audits revealed and confirmed disparities of care including patient/disease characteristics, treatment history, clinical practice metrics, and patient-centered measures. Follow-up chart audits conducted 6 months later measured changes in documented practice behavior. Action plans developed by the interprofessional teams as a result of this study intend to address sustainable reductions in health disparities among patients with MM to improve health equity and overall care., Implications for Case Management Practice: This implementation science initiative and data results have several implications for case managers caring for diverse patients with MM in both large health systems and smaller community practices. Results punctuate the importance of identifying and supporting diverse patients' individualized goals and preferences in their care journey to mitigate health inequity and maximize health outcomes. The value of working collaboratively as an interprofessional team is evident in the study results, as is the role of the case manager in appropriate resource allocation to mitigate health disparities. Lessons learned from this initiative may also be applied to other case management settings where complex care delivery and interprofessional teams are at work., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)