Yale School of Medicine, New Haven, Connecticut, USA.
Nanyang Technological University, Singapore, Singapore.
J Gastroenterol Hepatol. 2021 Feb;36(2):295-298. doi: 10.1111/jgh.15378.
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. On the operational level, AI-assisted clinical management should consider logistic challenges in care delivery, data management, and algorithmic stewardship. 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.
机器学习是人工智能 (AI) 的一个分支，是一组计算工具，可用于增强医学各个领域的临床护理水平。胃肠病学和肝脏病学利用多种信息来源，包括内镜检查的视觉发现、影像学成像、测压测试、基因组、蛋白质组和代谢组学。然而，临床护理非常复杂，需要深思熟虑的方法来最好地部署人工智能工具，以提高护理质量并为患者和提供者带来价值。在运营层面上，人工智能辅助的临床管理应该考虑到医疗服务提供中的物流挑战、数据管理和算法管理。在更广泛的社会层面上，仍有许多工作要做，以制定伦理、监管和报销框架。多学科方法和对人工智能工具的认识将为使用人工智能辅助工具指导和增强临床实践创造一个充满活力的生态系统。从光学增强内镜检查到风险分层的临床决策支持，人工智能工具将通过利用大量数据将护理个性化到合适的患者、合适的数量和合适的时间，从而有可能改变我们的实践。
