Evidence map›Paper›PMID 41340840›Full record

ArticleJMIR XR and spatial computing2025

Role of Augmented Reality in Tertiary Care: Qualitative Investigation Using Thematic Analysis.

Jacob Hobbs, Christopher Bull, Caroline Claisse, Mat Elameer, Richard Davison

Abstract read
In one paragraph

Article in JMIR XR and spatial computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jacob HobbsOpen Lab, School of Computing, Newcastle University, Floor 1, Urban Sciences Building, Newcastle Helix, Newcastle upon Tyne, NE4 5TG, United Kingdom.ORCID https://orcid.org/0000-0003-3186-2132
Christopher BullOpen Lab, School of Computing, Newcastle University, Floor 1, Urban Sciences Building, Newcastle Helix, Newcastle upon Tyne, NE4 5TG, United Kingdom.ORCID https://orcid.org/0000-0002-9811-4190
Caroline ClaisseOpen Lab, School of Computing, Newcastle University, Floor 1, Urban Sciences Building, Newcastle Helix, Newcastle upon Tyne, NE4 5TG, United Kingdom.ORCID https://orcid.org/0000-0003-4002-6136
Mat ElameerStroke Research Group, Institute for Ageing, Faculty of Medical Sciences Newcastle University, Newcastle upon Tyne, United Kingdom.ORCID https://orcid.org/0000-0003-2557-4863
Richard DavisonGames Lab, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.ORCID https://orcid.org/0000-0003-4085-6404

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: While augmented reality (AR) as a concept is not new, it is still an emerging technology with a wide range of applications that it could provide value for. In the medical field, AR is becoming ever more prevalent, but while it has been applied to various medical tasks, it is far from commonplace. Radiological imaging has been suggested as one of these applications, and the radiology workflow capacity crisis the United Kingdom's National Health Service is experiencing is a potential opportunity for technology to alleviate pressure. Understanding clinical stakeholders and current systems is important for identifying design opportunities for developing AR to enhance interactions and gain more from radiological images. Objective: This study had 3 key aims. First, to build an understanding of the field in the context of AR; second, to understand the stakeholders and workflows surrounding radiological images; and finally, to suggest how AR could integrate within these workflows and current practices in order to provide value. Methods: We conducted 14 interviews with hospital-based consultants in a range of specialties and then completed a thematic analysis on the transcripts in order to find trends that suggest what value AR could add to radiological imaging, where that value could be added, and who would benefit. We implemented reflexive thematic analysis to develop themes from across the interviews, which were then built on to suggest design implications. Results: We find that the need for efficiency in image evaluation is present across many roles, regardless of the clinical question, but consultants can be resistant to new technology. Additionally, we find that the current capability of AR technology could be of greater benefit to radiologists as opposed to surgeons or other practitioners. We discuss these findings for the development of AR applications and present 3 design implications that stand as our core contribution. Conclusions: We conclude with 3 design implications for the application of AR within radiological imaging based on the results of our thematic analysis and frame them within the Human-Computer Interaction and medical fields. The first design implication highlights efficiency and how AR has the potential to allow for quicker comprehension and measurements. Second, we suggest that the capability of AR tools should complement existing techniques and not simply replicate current ability in 3 dimensions. Finally, the integration of AR tools with existing workflows is crucial in the uptake of the technology in order not to negatively disrupt practice.

Indexed as

augmented realityclinical practiceradiologysurgerythematic analysis

Identifiers

PMID41340840
PMCPMC12671309

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.