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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.


DOI:10.3390/jcm14020549
PMID:39860554
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11765989/
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
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff49/11765989/d63929cb6bd4/jcm-14-00549-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff49/11765989/81ad557493dc/jcm-14-00549-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff49/11765989/eff854c98dca/jcm-14-00549-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff49/11765989/9361a18fe725/jcm-14-00549-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff49/11765989/1f9c27176adc/jcm-14-00549-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff49/11765989/d63929cb6bd4/jcm-14-00549-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff49/11765989/81ad557493dc/jcm-14-00549-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff49/11765989/eff854c98dca/jcm-14-00549-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff49/11765989/9361a18fe725/jcm-14-00549-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff49/11765989/1f9c27176adc/jcm-14-00549-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff49/11765989/d63929cb6bd4/jcm-14-00549-g005.jpg

相似文献

[1]
Explainable AI in Digestive Healthcare and Gastrointestinal Endoscopy.

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[2]
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[10]
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引用本文的文献

[1]
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[2]
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[3]
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本文引用的文献

[1]
Conversational LLM Chatbot ChatGPT-4 for Colonoscopy Boston Bowel Preparation Scoring: An Artificial Intelligence-to-Head Concordance Analysis.

Diagnostics (Basel). 2024-11-13

[2]
Deep Learning and High-Resolution Anoscopy: Development of an Interoperable Algorithm for the Detection and Differentiation of Anal Squamous Cell Carcinoma Precursors-A Multicentric Study.

Cancers (Basel). 2024-5-17

[3]
Explainable AI-driven model for gastrointestinal cancer classification.

Front Med (Lausanne). 2024-4-15

[4]
Enabling large-scale screening of Barrett's esophagus using weakly supervised deep learning in histopathology.

Nat Commun. 2024-3-11

[5]
Large language models: a primer and gastroenterology applications.

Therap Adv Gastroenterol. 2024-2-22

[6]
The role of large language models in medical image processing: a narrative review.

Quant Imaging Med Surg. 2024-1-3

[7]
Deep Learning for Automatic Diagnosis and Morphologic Characterization of Malignant Biliary Strictures Using Digital Cholangioscopy: A Multicentric Study.

Cancers (Basel). 2023-10-1

[8]
Exploring the challenge of early gastric cancer diagnostic AI system face in multiple centers and its potential solutions.

J Gastroenterol. 2023-10

[9]
Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images.

Int J Med Inform. 2023-9

[10]
The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents.

Medicina (Kaunas). 2023-4-18

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