Extended Reality in Industry and Healthcare: Current Trends and Future Perspectives
Bibliographic record
Abstract
Abstract Extended reality (XR) technologies are no longer peripheral innovations but emerging cornerstones of human–technology interaction across critical sectors. This article takes the position that engineering and healthcare represent the most mature and strategically relevant domains for XR adoption, given their safety-critical nature, intensive training requirements, and strong alignment with the human-centric visions of Industry 5.0 and Healthcare 5.0. We synthesize evidence from product design, manufacturing, training, and patient care to demonstrate how XR is reshaping workflows, skills, and therapeutic practices. Beyond surveying applications, we argue that the future of XR depends on its integration with artificial intelligence, digital twins, and multisensory feedback, converging into systems capable of perceiving, reasoning, and adapting to complex physical and human environments. We contend that widespread adoption will remain limited without open standards, validated protocols, and robust evaluation frameworks addressing safety, interoperability, and data governance. By framing XR as both a technological enabler and a societal imperative, this position article calls for coordinated action among researchers, practitioners, and policymakers to realize XR’s role in building sustainable, personalized, and participatory innovation ecosystems.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".