Evidence map›Paper›PMID 40107682›Full record

ArticleBMJ open2025

Using artificial intelligence to improve healthcare delivery in select allied health disciplines: a scoping review protocol.

Kalpana Raghunathan, Meg E Morris, Tafheem A Wani, Kristina Edvardsson, Casey Peiris, Sally Fowler-Davis, Jonathan P McKercher, Sharon Bourke, Saadia Danish, Jacqueline Johnston and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open, 2025. 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. Review
  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

16 authors.

Kalpana RaghunathanSchool of Computing, Engineering and Mathematical Sciences, La Trobe University, Bundoora, Victoria, Australia.
Meg E MorrisCare Economy Research Institute, La Trobe University, Bundoora, Victoria, Australia m.morris@latrobe.edu.au.ORCID http://orcid.org/0000-0002-0114-4175
Tafheem A WaniCare Economy Research Institute, La Trobe University, Bundoora, Victoria, Australia.
Kristina EdvardssonCare Economy Research Institute, La Trobe University, Bundoora, Victoria, Australia.
Casey PeirisAcademic and Research Collaborative in Health, La Trobe University, Melbourne, Victoria, Australia.
Sally Fowler-DavisHealth, Medicine and Social Care, Anglia Ruskin University, Cambridge, East of England, UK.
Jonathan P McKercherCare Economy Research Institute, La Trobe University, Bundoora, Victoria, Australia.ORCID http://orcid.org/0000-0002-8839-8353
Sharon BourkeCare Economy Research Institute, La Trobe University, Bundoora, Victoria, Australia.
Saadia DanishCare Economy Research Institute, La Trobe University, Bundoora, Victoria, Australia.
Jacqueline JohnstonCare Economy Research Institute, La Trobe University, Bundoora, Victoria, Australia.
Nompilo MoyoCare Economy Research Institute, La Trobe University, Bundoora, Victoria, Australia.
Julia Gilmartin-ThomasAcademic and Research Collaborative in Health, La Trobe University, Melbourne, Victoria, Australia.
Hazel Wei Fen HengAllied Health, Northern Hospital Epping, Epping, Victoria, Australia.ORCID http://orcid.org/0000-0001-8662-7063
Ken HoCare Economy Research Institute, La Trobe University, Bundoora, Victoria, Australia.
Joanne Joyce-McCoachLa Trobe University, Melbourne, Victoria, Australia.
Claire ThwaitesCare Economy Research Institute, La Trobe University, Bundoora, Victoria, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMethods to adopt artificial intelligence (AI) in healthcare clinical practice remain unclear. The potential for rapid integration of AI-enabled technologies across healthcare settings coupled with the growing digital divide in the health sector highlights the need to examine AI use by health professionals, especially in allied health disciplines with emerging AI use such as physiotherapy, occupational therapy, speech pathology, podiatry and dietetics. This protocol details the methodology for a scoping review on the use of AI-enabled technology in sectors of the allied health workforce. The research question is 'How is AI used by sectors of the allied health workforce to improve patient safety, quality of care and outcomes, and what is the quality of evidence supporting this use?' METHODS AND ANALYSIS: The review will follow the Joanna Briggs Institute scoping review guidelines. Databases will be searched from 17 to 24 March 2025 and will include PubMed/Medline, Embase, PsycINFO and Cummulative Index to Nursing and Allied Health Literature databases. Dual screening against inclusion criteria will be applied for study selection. Peer-reviewed articles reporting primary research in allied healthcare published in English within the last 10 years will be included. Studies will be evaluated using the Quality Assessment with Diverse Studies tool. The review will map the existing literature and identify key themes related to the use of AI in the disciplines of physiotherapy, occupational therapy, speech pathology, podiatry and dietetics. ETHICS AND DISSEMINATION: No ethics approval will be sought, as only secondary research outputs will be used. Findings will be disseminated through peer-reviewed publication and presentations at workshops and conferences. TRIAL REGISTRATION NUMBER: Open Science Framework Protocol Registration https://osf.io/r7t4s.

Indexed as

Allied Health PersonnelArtificial IntelligenceDelivery of Health CareHumansResearch DesignScoping Reviews as TopicArtificial IntelligenceHealth ServicesHealth WorkforcePUBLIC HEALTH

Identifiers

PMID40107682
PMCPMC11927405

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.