Evidence map›Paper›PMID 37287484›Full record

ArticlePaediatrics & child health2023

The promises and challenges of clinical AI in community paediatric medicine.

Devin Singh, Sujay Nagaraj, Ryan Daniel, Colleen Flood, Dina Kulik, Robert Flook, Anna Goldenberg, Michael Brudno, Ian Stedman

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Paediatrics & child health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Devin SinghHospital for Sick Children, Toronto, Ontario, Canada.
Sujay NagarajTemerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Ryan DanielTemerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Colleen FloodCentre for Health Law, Policy and Ethics, University of Ottawa, Ottawa, Ontario, Canada.
Dina KulikHospital for Sick Children, Toronto, Ontario, Canada.
Robert FlookDepartment of Family Medicine, University of Alberta, Edmonton, Alberta, Canada.
Anna GoldenbergHospital for Sick Children, Toronto, Ontario, Canada.
Michael BrudnoDepartment of Computer Science, University of Toronto, Toronto, Ontario, Canada.
Ian StedmanSchool of Public Policy and Administration, York University, Toronto, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The widespread adoption of virtual care technologies has quickly reshaped healthcare operations and delivery, particularly in the context of community medicine. In this paper, we use the virtual care landscape as a point of departure to envision the promises and challenges of artificial intelligence (AI) in healthcare. Our analysis is directed towards community care practitioners interested in learning more about how AI can change their practice along with the critical considerations required to integrate AI into their practice. We highlight examples of how AI can enable access to new sources of clinical data while augmenting clinical workflows and healthcare delivery. AI can help optimize how and when care is delivered by community practitioners while also improving practice efficiency, accessibility, and the overall quality of care. Unlike virtual care, however, AI is still missing many of the key enablers required to facilitate adoption into the community care landscape and there are challenges we must consider and resolve for AI to successfully improve healthcare delivery. We discuss several critical considerations, including data governance in the clinic setting, healthcare practitioner education, regulation of AI in healthcare, clinician reimbursement, and access to both technology and the internet.

Indexed as

Artificial IntelligenceCommunity medicinePaediatricsRegulation

Identifiers

PMID37287484
PMCPMC10243963

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.