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消化健康与胃肠内镜检查中的可解释人工智能

Explainable AI in Digestive Healthcare and Gastrointestinal Endoscopy.

作者信息

Mascarenhas Miguel, Mendes Francisco, Martins Miguel, Ribeiro Tiago, Afonso João, Cardoso Pedro, Ferreira João, Fonseca João, Macedo Guilherme

机构信息

Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal.

WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal.

出版信息

J Clin Med. 2025 Jan 16;14(2):549. doi: 10.3390/jcm14020549.

Abstract

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.

摘要

将基于人工智能的工具纳入医疗保健的一个重要障碍是它们与所谓的“黑箱医学”相关联,这一概念因工具的复杂性以及难以理解其如何做出决策而产生。如果出现任何错误,这种情况可能会损害临床医生对这些工具的信任,而且无法解释决策过程可能会影响他们与患者的关系。可解释人工智能(XAI)旨在通过帮助用户更好地理解人工智能模型如何得出结论来克服这一局限性,从而增强对所做决策的信任。本综述首先定义了XAI的基本概念,介绍了可用工具及其如何造福消化健康领域。提供了XAI在消化健康领域的应用实例,并提出了未来可能的用途。此外,还讨论了在开发这些工具时必须建立的监管框架方面以及必须牢记的伦理问题。最后,我们考虑了该技术为确保获得最佳效益所面临的挑战,强调了在该领域对XAI的使用进行更多研究的必要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff49/11765989/81ad557493dc/jcm-14-00549-g001.jpg

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