Evidence map›Paper›PMID 40933813›Full record

ArticleFrontiers in digital health2025

Artificial intelligence and health empowerment in rural communities and landslide- or avalanche-isolated contexts: real case at a fictitious location.

Rune Johan Krumsvik, Vegard Slettvoll

Abstract read
In one paragraph

Article in Frontiers in digital health, 2025. 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

2 authors.

Rune Johan KrumsvikFaculty of Psychology, University of Bergen, Bergen, Norway.
Vegard SlettvollFaculty of Medicine, University of Bergen, Bergen, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Through a series of case studies, we have pretested the capabilities and reliability of the Large Language Models (LLM), Generative Pre-trained Transformer 4 (GPT-4) and OpenAI o3 reasoning model (o3) in educational and healthcare contexts. Based on this knowledge, we took a step further by testing these technologies in an authentic patient case set in a fictitious location. The context for this brief case report relates to the fact that, in the first quarter of 2025, fewer patients lacked an assigned GP compared to previous years-a positive trend. However, this offers little relief to those cut off from GP care due to their rural location or because of landslides and extreme weather. This case highlights the need for knowledge-based preparedness and alternative health empowerment pathways in rural Norway. This brief case report describes a single 16-year-old boy (

Indexed as

artificial intelligenceavalanchesbrief case reporthealth empowermentisolated villageslandsliderural healthcaresimulated rural setting

Identifiers

PMID40933813
PMCPMC12417106

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.