Evidence map›Paper›PMID 36526854›Full record

ArticlePediatric research2023

From computer to bedside, involving neonatologists in artificial intelligence models for neonatal medicine.

Daniel Vijlbrief, Jeroen Dudink, Wouter van Solinge, Manon Benders, Saskia Haitjema

Abstract read
PubMed Publisher
In one paragraph

Article in Pediatric research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Daniel VijlbriefDepartment of Neonatology, University Medical Center Utrecht, Utrecht University, UMC Utrecht, Utrecht, The Netherlands. D.C.Vijlbrief@umcutrecht.nl.ORCID 0000-0002-2682-7386
Jeroen DudinkDepartment of Neonatology, University Medical Center Utrecht, Utrecht University, UMC Utrecht, Utrecht, The Netherlands.
Wouter van SolingeCentral Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, UMC Utrecht, Utrecht, The Netherlands.
Manon BendersDepartment of Neonatology, University Medical Center Utrecht, Utrecht University, UMC Utrecht, Utrecht, The Netherlands.
Saskia HaitjemaCentral Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, UMC Utrecht, Utrecht, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, data have become the main driver of medical innovation. With increased availability and decreased price of storage and computing power, the potential for improvement in care is enormous. Many data-driven explorations have started. However, the actual implementation of artificial intelligence in healthcare remains scarce. We describe essential elements during a computer-to-bedside process in a data science project that support the crucial role of the neonatologist. IMPACT: There is a great potential for data science in neonatal medicine. Multidisciplinary teams form the foundation of a data science project. Domain experts will need to play a pivotal role. We need an open learning environment.

Indexed as

Artificial IntelligenceMedicineComputersDelivery of Health CareHumansInfant, NewbornNeonatologists

Identifiers

PMID36526854

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.