Evidence map›Paper›PMID 34790948›Full record

ArticleAI and ethics2022

The ethical use of high-performance computing and artificial intelligence: fighting COVID-19 at Barcelona Supercomputing Center.

Ulises Cortés, Atia Cortés, Dario Garcia-Gasulla, Raquel Pérez-Arnal, Sergio Álvarez-Napagao, Enric Àlvarez

Abstract read
In one paragraph

Article in AI and ethics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

6 authors.

Ulises CortésUniversitat Politècnica de Catalunya, Edifici Omega 205, Jordi Girona 29, 08034 Barcelona, Spain.ORCID 0000-0003-0192-3096
Atia CortésBarcelona Supercomputing Center, Edifici Omega 201, Jordi Girona 1 and 3, 08034 Barcelona, Spain.
Dario Garcia-GasullaBarcelona Supercomputing Center, Edifici Omega 201, Jordi Girona 1 and 3, 08034 Barcelona, Spain.
Raquel Pérez-ArnalBarcelona Supercomputing Center, Edifici Omega 201, Jordi Girona 1 and 3, 08034 Barcelona, Spain.
Sergio Álvarez-NapagaoBarcelona Supercomputing Center, Edifici Omega 201, Jordi Girona 1 and 3, 08034 Barcelona, Spain.
Enric ÀlvarezUniversitat Politècnica de Catalunya, Edifici Omega 205, Jordi Girona 29, 08034 Barcelona, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has created an extraordinary medical, economic and humanitarian emergency. Artificial intelligence, in combination with other digital technologies, is being used as a tool to support the fight against the viral pandemic that has affected the entire world since the beginning of 2020. Barcelona Supercomputing Center collaborates in the battle against the coronavirus in different areas: the application of bioinformatics for the research on the virus and its possible treatments, the use of artificial intelligence, natural language processing and big data techniques to analyse the spread and impact of the pandemic, and the use of the MareNostrum 4 supercomputer to enable massive analysis on COVID-19 data. Many of these activities have included the use of personal and sensitive data of citizens, which, even during a pandemic, should be treated and handled with care. In this work we discuss our approach based on an ethical, transparent and fair use of this information, an approach aligned with the guidelines proposed by the European Union.

Indexed as

Artificial intelligenceCOVID-19Data protectionEthicsHigh-performance computingMachine learning

Identifiers

PMID34790948
PMCPMC8101339

What OpenQuestion holds

Textmetadata
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.