Background: Rehabilitation using extended reality (XR) technologies can be used to address the growing shortage of health care staff, but the adoption level remains low. The current literature has given some first insights into what drives patients and clinicians to (not yet) adopt XR-rehabilitation tools. However, it does not sufficiently take into account that these tools will only be widely adopted if 3 types of actors collectively commit to it: developers must develop a tool, clinicians must prescribe it, and patients must use it. Because the preferences of these 3 actors may not always align, we aim to provide the first multi-actor insight into adoption. Objective: This research aims (1) to determine what drives patients, clinicians, and developers to adopt or develop XR-rehabilitation tools and (2) to determine if and how these drivers align or misalign. Methods: We searched PubMed, Embase, SCOPUS, and Web of Science with four search term categories: (1) types of rehabilitation care, (2) XR technologies, (3) adoption constructs, and (4) behavioral drivers. Using these search terms, we identified 1164 results, of which we included 64 in our review. All relevant empirical results within these papers were structured using the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework. Results: After exploring the adoption drivers of patients, clinicians, and developers, we identified 3 potential misalignments among these actors. The first possible misalignment is that clinicians may have much higher standards for a tool’s medical efficacy. Because of this, they refuse to prescribe a medically less effective tool that would have matched the experience needs of patients and developers. The second possible misalignment is that clinicians value their work experience, while this is not a relevant factor for patients. Because using XR-rehabilitation tools can negatively impact a clinician’s work experience, they may decide not to use a tool that patients and developers would have liked to use and develop. The third possible misalignment is that the patients’ and clinicians’ limited ability or willingness to pay may hinder the developer’s economies of scale. Developers currently face high development costs, which they can recover by letting patients or clinicians pay for the tool. But these actors are not always able or willing to do so. As a result, developers may struggle to gain profitability, which limits the supply of XR tools. Conclusions: Our scoping review provides initial evidence that differences in the behavioral drivers of patients, clinicians, and developers may lead to misalignments that hinder the adoption of XR-based rehabilitation tools. Scholars can use this review to further investigate potential misalignments between relevant stakeholders and how to resolve them. We encourage developers and regulatory institutes to collaboratively investigate the feasibility of new revenue models and product offerings to increase the adoption of XR-rehabilitation tools.
Explainable AI in kidney stone detection and segmentation: a mini review
Kidney stones are one of the most common renal disorders that can produce severe complications if not diagnosed and treated early. Recently, advances in AI