An important impediment to the incorporation of artificial intelligence-based tools into healthcare is their association with so-called black box medicine, a concept arising due to their complexity and the difficulties in understanding how they reach a decision. This situation may compromise the clinician's trust in these tools, should any errors occur, and the inability to explain how decisions are reached may affect their relationship with patients. Explainable AI (XAI) aims to overcome this limitation by facilitating a better understanding of how AI models reach their conclusions for users, thereby enhancing trust in the decisions reached. This review first defined the concepts underlying XAI, establishing the tools available and how they can benefit digestive healthcare. Examples of the application of XAI in digestive healthcare were provided, and potential future uses were proposed. In addition, aspects of the regulatory frameworks that must be established and the ethical concerns that must be borne in mind during the development of these tools were discussed. Finally, we considered the challenges that this technology faces to ensure that optimal benefits are reaped, highlighting the need for more research into the use of XAI in this field.
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http://dx.doi.org/10.3390/jcm14020549 | DOI Listing |
Gastroenterology
February 2025
Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA Kaiser Permanente Center for Health Equity and Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, California; Department of Medicine, Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California.
Br J Hosp Med (Lond)
January 2025
Department of Gastroenterology, Nantong First People's Hospital, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China.
Artificial intelligence (AI), with advantages such as automatic feature extraction and high data processing capacity and being unaffected by fatigue, can accurately analyze images obtained from colonoscopy, assess the quality of bowel preparation, and reduce the subjectivity of the operating physician, which may help to achieve standardization and normalization of colonoscopy. In this study, we aimed to explore the value of using an AI-driven intestinal image recognition model to evaluate intestinal preparation before colonoscopy. In this retrospective analysis, we analyzed the clinical data of 98 patients who underwent colonoscopy in Nantong First People's Hospital from May 2023 to October 2023.
View Article and Find Full Text PDFNutrients
January 2025
Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
Background/objectives: This protocol describes a study to investigate the feasibility and preliminary efficacy of a novel Teaching Kitchen Multisite Trial (TK-MT) for adults with cardiometabolic abnormalities. The TK-MT protocol describes a hybrid lifestyle intervention combining in-person and virtual instruction in culinary skills, nutrition education, movement, and mindfulness with community support and behavior change strategies. This 18-month-long randomized controlled trial aims to evaluate the feasibility of implementing a 12-month, 24 class program, assess preliminary study efficacy, and identify barriers and facilitators to implementation.
View Article and Find Full Text PDFJ Clin Med
January 2025
Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal.
An important impediment to the incorporation of artificial intelligence-based tools into healthcare is their association with so-called black box medicine, a concept arising due to their complexity and the difficulties in understanding how they reach a decision. This situation may compromise the clinician's trust in these tools, should any errors occur, and the inability to explain how decisions are reached may affect their relationship with patients. Explainable AI (XAI) aims to overcome this limitation by facilitating a better understanding of how AI models reach their conclusions for users, thereby enhancing trust in the decisions reached.
View Article and Find Full Text PDFMicroorganisms
January 2025
Division of Gastroenterology, Department of Internal Medicine and Gastrointestinal Cancer Center, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea.
Studies on the gastric microbiota associated with gastric precancerous lesions remain limited. This study aimed to profile the gastric mucosal microbiota in patients with -negative precancerous lesions. Gastric mucosal samples were obtained from 67 -negative patients, including those with chronic gastritis (CG), intestinal metaplasia (IM), and dysplasia.
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