Mixed Reality Platforms in Telehealth Delivery: Scoping Review
Bibliographic record
Abstract
BACKGROUND: The distinctive features of the digital reality platforms, namely augmented reality (AR), virtual reality (VR), and mixed reality (MR) have extended to medical education, training, simulation, and patient care. Furthermore, this digital reality technology seamlessly merges with information and communication technology creating an enriched telehealth ecosystem. This review provides a composite overview of the prospects of telehealth delivered using the MR platform in clinical settings. OBJECTIVE: This review identifies various clinical applications of high-fidelity digital display technology, namely AR, VR, and MR, delivered using telehealth capabilities. Next, the review focuses on the technical characteristics, hardware, and software technologies used in the composition of AR, VR, and MR in telehealth. METHODS: We conducted a scoping review using the methodological framework and reporting design using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Full-length articles in English were obtained from the Embase, PubMed, and Web of Science databases. The search protocol was based on the following keywords and Medical Subject Headings to obtain relevant results: "augmented reality," "virtual reality," "mixed-reality," "telemedicine," "telehealth," and "digital health." A predefined inclusion-exclusion criterion was developed in filtering the obtained results and the final selection of the articles, followed by data extraction and construction of the review. RESULTS: We identified 4407 articles, of which 320 were eligible for full-text screening. A total of 134 full-text articles were included in the review. Telerehabilitation, telementoring, teleconsultation, telemonitoring, telepsychiatry, telesurgery, and telediagnosis were the segments of the telehealth division that explored the use of AR, VR, and MR platforms. Telerehabilitation using VR was the most commonly recurring segment in the included studies. AR and MR has been mainly used for telementoring and teleconsultation. The most important technical features of digital reality technology to emerge with telehealth were virtual environment, exergaming, 3D avatars, telepresence, anchoring annotations, and first-person viewpoint. Different arrangements of technology-3D modeling and viewing tools, communication and streaming platforms, file transfer and sharing platforms, sensors, high-fidelity displays, and controllers-formed the basis of most systems. CONCLUSIONS: This review constitutes a recent overview of the evolving digital AR and VR in various clinical applications using the telehealth setup. This combination of telehealth with AR, VR, and MR allows for remote facilitation of clinical expertise and further development of home-based treatment. This review explores the rapidly growing suite of technologies available to users within the digital health sector and examines the opportunities and challenges they present.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.023 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".