Evidence map›Paper›PMID 35451963›Full record

ArticleJMIR formative research2022

Integration of Augmented Reality and Brain-Computer Interface Technologies for Health Care Applications: Exploratory and Prototyping Study.

Anya Andrews

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2022. 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
2.0field-weighted citation impact, top 14% of its field
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, 17 citations in OpenAlex.

  1. Review
  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

1 author at 1 institution in 1 country.

Anya AndrewsDepartment of Internal Medicine, College of Medicine, University of Central Florida, Orlando, FL, United States.ORCID https://orcid.org/0000-0001-9205-5276
University of Central Florida · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAugmented reality (AR) and brain-computer interface (BCI) are promising technologies that have a tremendous potential to revolutionize health care. While there has been a growing interest in these technologies for medical applications in the recent years, the combined use of AR and BCI remains a fairly unexplored area that offers significant opportunities for improving health care professional education and clinical practice. This paper describes a recent study to explore the integration of AR and BCI technologies for health care applications.

objectiveThe described effort aims to advance an understanding of how AR and BCI technologies can effectively work together to transform modern health care practice by providing new mechanisms to improve patient and provider learning, communication, and shared decision-making.

methodsThe study methods included an environmental scan of AR and BCI technologies currently used in health care, a use case analysis for a combined AR-BCI capability, and development of an integrated AR-BCI prototype solution for health care applications.

resultsThe study resulted in a novel interface technology solution that enables interoperability between consumer-grade wearable AR and BCI devices and provides the users with an ability to control digital objects in augmented reality using neural commands. The article discusses this novel solution within the context of practical digital health use cases developed during the course of the study where the combined AR and BCI technologies are anticipated to produce the most impact.

conclusionsAs one of the pioneering efforts in the area of AR and BCI integration, the study presents a practical implementation pathway for AR-BCI integration and provides directions for future research and innovation in this area.

Indexed as

augmented realitybrain-computer interfaceclinical performance supportdigital healthhealth professional educationinterprofessional teamworkmHealthmobile healthpatient educationtechnology integration

Identifiers

PMID35451963
PMCPMC9073621
OpenAlexW4224210158

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.