Smart Endoscopy Is Greener Endoscopy: Leveraging Artificial Intelligence and Blockchain Technologies to Drive Sustainability in Digestive Health Care.

artificial intelligence blockchain capsule endoscopy carbon offsetting convoluted neural networks deep learning digestive health care greenhouse gases sustainability

Journal

Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402

Informations de publication

Date de publication:
08 Dec 2023
Historique:
received: 20 10 2023
revised: 14 11 2023
accepted: 25 11 2023
medline: 22 12 2023
pubmed: 22 12 2023
entrez: 22 12 2023
Statut: epublish

Résumé

The surge in the implementation of artificial intelligence (AI) in recent years has permeated many aspects of our life, and health care is no exception. Whereas this technology can offer clear benefits, some of the problems associated with its use have also been recognised and brought into question, for example, its environmental impact. In a similar fashion, health care also has a significant environmental impact, and it requires a considerable source of greenhouse gases. Whereas efforts are being made to reduce the footprint of AI tools, here, we were specifically interested in how employing AI tools in gastroenterology departments, and in particular in conjunction with capsule endoscopy, can reduce the carbon footprint associated with digestive health care while offering improvements, particularly in terms of diagnostic accuracy. We address the different ways that leveraging AI applications can reduce the carbon footprint associated with all types of capsule endoscopy examinations. Moreover, we contemplate how the incorporation of other technologies, such as blockchain technology, into digestive health care can help ensure the sustainability of this clinical speciality and by extension, health care in general.

Identifiants

pubmed: 38132209
pii: diagnostics13243625
doi: 10.3390/diagnostics13243625
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Auteurs

Miguel Mascarenhas (M)

Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal.
Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal.
WGO Training Center, 4200-437 Porto, Portugal.

Tiago Ribeiro (T)

Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal.
WGO Training Center, 4200-437 Porto, Portugal.

João Afonso (J)

Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal.
WGO Training Center, 4200-437 Porto, Portugal.

Francisco Mendes (F)

Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal.
WGO Training Center, 4200-437 Porto, Portugal.

Pedro Cardoso (P)

Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal.
WGO Training Center, 4200-437 Porto, Portugal.

Miguel Martins (M)

Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal.
WGO Training Center, 4200-437 Porto, Portugal.

João Ferreira (J)

Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal.

Guilherme Macedo (G)

Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal.
Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal.
WGO Training Center, 4200-437 Porto, Portugal.

Classifications MeSH