Evidence map›Paper›PMID 40444646›Full record

ArticleThe Australian journal of rural health2025

When One Size Does not Fit All-Artificial Intelligence in Australian Rural Health.

Lewis Hains, Joshua G Kovoor, Brandon Stretton, Aashray K Gupta, Ammar Zaka, Gavin Carmichael, John M Kefalianos, Win Le Shwe Sin Ei, Alasdair Leslie, Andrew Booth and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Australian journal of rural health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Artificial intelligence in rural health care: A framework for responsible adoption, readiness, and access.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2026
    Article
  4. 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

17 authors.

Lewis HainsAdelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.ORCID 0009-0003-0577-9105
Joshua G KovoorAdelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.
Brandon StrettonAdelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.
Aashray K GuptaAdelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.ORCID 0000-0002-8038-0378
Ammar ZakaGold Coast University Hospital, Gold Coast, Queensland, Australia.
Gavin CarmichaelBallarat Base Hospital, Ballarat, Victoria, Australia.ORCID 0009-0009-2143-3896
John M KefalianosBallarat Base Hospital, Ballarat, Victoria, Australia.
Win Le Shwe Sin EiBallarat Base Hospital, Ballarat, Victoria, Australia.
Alasdair LeslieAdelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.
Andrew BoothRoyal Adelaide Hospital, Adelaide, South Australia, Australia.
Shrirajh SatheakeerthyRoyal Adelaide Hospital, Adelaide, South Australia, Australia.
Alexander BeathBallarat Base Hospital, Ballarat, Victoria, Australia.
Yasser ArafatBallarat Base Hospital, Ballarat, Victoria, Australia.
Mathew O JacobBallarat Base Hospital, Ballarat, Victoria, Australia.
Martin BrueningAdelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.
Weng Onn ChanAdelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.
Stephen BacchiAdelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia.ORCID 0000-0001-5130-8628

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsArtificial intelligence (AI) is having an increasing impact on many aspects of our day-to-day lives. This change is also true in healthcare, with various tools being developed to hasten burdensome administrative tasks and increase overall healthcare efficiency, particularly in metropolitan centres. CONTEXT: AI has remained comparatively clear of rural, regional and remote Australian hospitals, where it has the potential to provide significant benefits. Like previous health technology implementations, rural workforce requirements for AI maintenance and support may hinder AI deployment in these areas. While AI has been implemented successfully in metropolitan areas, these models may have limited translatability to rural health settings with significantly different administrative and healthcare systems. APPROACH: AI may assist with key issues in rural centres such as resource allocation and timely patient transfer for higher level care. While the potential benefits of AI in rural centres are clear, one must consider key factors in rural centres that may limit the success of AI in these hospitals. Smaller rural populations may limit the ability to train location-specific models, and connectivity issues may impede their effective use.

conclusionSpecific efforts are required to realise potential benefits of medical AI for rural Australia; addressing connectivity and workforce issues in rural areas is vital to allow for AI and large language models to help benefit rural centres.

Indexed as

Artificial IntelligenceRural Health ServicesAustraliaHumansRural Population

Identifiers

PMID40444646
PMCPMC12123659

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.