Augmented Reality in Real-time Telemedicine and Telementoring: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Over the last decade, augmented reality (AR) has emerged in health care as a tool for visualizing data and enhancing simulation learning. AR, which has largely been explored for communication and collaboration in nonhealth contexts, could play a role in shaping future remote medical services and training. This review summarized existing studies implementing AR in real-time telemedicine and telementoring to create a foundation for health care providers and technology developers to understand future opportunities in remote care and education. OBJECTIVE: This review described devices and platforms that use AR for real-time telemedicine and telementoring, the tasks for which AR was implemented, and the ways in which these implementations were evaluated to identify gaps in research that provide opportunities for further study. METHODS: We searched PubMed, Scopus, Embase, and MEDLINE to identify English-language studies published between January 1, 2012, and October 18, 2022, implementing AR technology in a real-time interaction related to telemedicine or telementoring. The search terms were "augmented reality" OR "AR" AND "remote" OR "telemedicine" OR "telehealth" OR "telementoring." Systematic reviews, meta-analyses, and discussion-based articles were excluded from analysis. RESULTS: A total of 39 articles met the inclusion criteria and were categorized into themes of patient evaluation, medical intervention, and education. In total, 20 devices and platforms using AR were identified, with common features being the ability for remote users to annotate, display graphics, and display their hands or tools in the local user's view. Common themes across the studies included consultation and procedural education, with surgery, emergency, and hospital medicine being the most represented specialties. Outcomes were most often measured using feedback surveys and interviews. The most common objective measures were time to task completion and performance. Long-term outcome and resource cost measurements were rare. Across the studies, user feedback was consistently positive for perceived efficacy, feasibility, and acceptability. Comparative trials demonstrated that AR-assisted conditions had noninferior reliability and performance and did not consistently extend procedure times compared with in-person controls. CONCLUSIONS: Studies implementing AR in telemedicine and telementoring demonstrated the technology's ability to enhance access to information and facilitate guidance in multiple health care settings. However, AR's role as an alternative to current telecommunication platforms or even in-person interactions remains to be validated, with many disciplines and provider-to-nonprovider uses still lacking robust investigation. Additional studies comparing existing methods may offer more insight into this intersection, but the early stage of technical development and the lack of standardized tools and adoption have hindered the conduct of larger longitudinal and randomized controlled trials. Overall, AR has the potential to complement and advance the capabilities of remote medical care and learning, creating unique opportunities for innovator, provider, and patient involvement.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".