Evidence map›Paper›PMID 42558155›Full record

ArticleJournal of medical extended reality2024

Artificial Intelligence Enhanced Virtual Reality: A Personalized, Multilingual Approach to Pain Management.

Oranicha Jumreornvong, Hanchen Mao, Gonzalo Povea Galdo, Taraf Jaro, Kaitlyn Schuler, Christopher L Robinson, Qing Zhao Ruan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical extended reality, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

7 authors.

Oranicha JumreornvongDepartment of Human Performance and Rehabilitation, Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID https://orcid.org/0000-0001-7327-9514
Hanchen MaoHigh School of Health Profession and Human Services, New York, New York, USA.
Gonzalo Povea GaldoDepartment of Biomedical Engineering, Universidad Peruana Cayetano Heredia, San Martin de Porres, Peru.
Taraf JaroRoyal College of Surgeons in Ireland Medical University in Bahrain, Busaiteen, Bahrain.
Kaitlyn SchulerDepartment of Psychology, University of North Carolina Wilmington, Wilmington, North Carolina, USA.
Christopher L RobinsonDepartment of Anesthesiology, Perioperative, and Pain Medicine, Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Qing Zhao RuanUnited States Department of Veterans Affair, The Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this commentary, we introduce the artificial intelligence (AI) integration of generative virtual reality (VR) and multilingual voice cloning as a novel approach to personalized pain management. Globally, pain impacts billions of people daily. AI generated VR technology has the potential to modulate pain and enhance cognitive engagement through rapid design and personalization, and AI voice cloning uses familiar, multilingual voices to offer both neurological and psychological support. Through demonstration of our prototypes' videos, we aim to explain how a combination of personalized AI text to 3-dimensional generated VR landscape and AI cloned voices could potentially reduce pain by providing personalized, emotionally supportive treatment that targets psychological and emotional factors in chronic pain. Our next steps include conducting feasibility and clinical trials to validate the technology's effectiveness across various cultural and health care settings.

Indexed as

artificial intelligencechronic paindiversitypain psychologyvirtual reality

Identifiers

PMID42558155
PMCPMC13387217

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.