Evidence map›Paper›PMID 38132209›Full record

ReviewDiagnostics (Basel, Switzerland)2023

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

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

Abstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Miguel MascarenhasFaculty of Medicine, University of Porto, 4200-319 Porto, Portugal.ORCID 0000-0002-0340-0830
Tiago RibeiroPrecision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal.
João AfonsoPrecision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal.
Francisco MendesPrecision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal.ORCID 0000-0002-5890-7049
Pedro CardosoPrecision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal.ORCID 0000-0001-9427-5635
Miguel MartinsPrecision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal.ORCID 0000-0002-0484-4804
João FerreiraFaculty of Engineering, University of Porto, 4200-465 Porto, Portugal.
Guilherme MacedoFaculty of Medicine, University of Porto, 4200-319 Porto, Portugal.ORCID 0000-0002-9387-9872

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

artificial intelligenceblockchaincapsule endoscopycarbon offsettingconvoluted neural networksdeep learningdigestive health caregreenhouse gasessustainability

Identifiers

PMID38132209
PMCPMC10743290

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.