Socio-technical barriers affecting large-scale deployment of AI-enabled wearable medical devices among the ageing population in China
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In some studies, patients reported the need to assess the usefulness of the AI before using it and had concerns around the quality and accuracy of the output, thereby questioning the value of AI as a whole.
“In some studies, patients reported the need to assess the usefulness of the AI before using it and had concerns around the quality and accuracy of the output, thereby questioning the value of AI as a whole [39,42,52-55].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [53]
Section 4.6 (p. 24) reports that elderly participants and their families distrusted AI-enabled wearable medical devices and questioned their clinical efficiency; family members also doubted whether advertised AI features provided useful services. Section 4.3 (p. 20) raises the possibility of misleading analytic results, but in relation to clinicians. The paper does not report patients saying they needed to assess AI's usefulness before use or questioning the value of AI as a whole. (LLM, conf=0.85)
Experimental — accuracy not yet measuredFor AI apps, data not being integrated into the health care system or workflow was seen as a barrier for patients to adopt the solution.
“For other types of AI solutions, such as apps, the data not being integrated into the health care system or workflow was seen as a barrier for patients to adopt the solution [42,53].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [53]
The Further discussion (p. 27) links a lack of collaboration between device providers and public health organisations to elderly users’ distrust and resistance to using AI-enabled wearable devices. It does not identify data failing to integrate into healthcare systems or workflows as the barrier, nor does it establish this finding for AI apps generally. (LLM, conf=0.82)
Experimental — accuracy not yet measuredLack of accuracy, doubts about unsafe results, privacy breaches, patients' perceptions and acceptance of automation, and uncertainty about developers' reliability, availability, usability, and perceived usefulness were found to be obstacles to gaining trust.
“Lack of accuracy; doubts about unsafe results; privacy breaches; patients’ perceptions and acceptance of automation; and uncertainty about developers’ reliability, availability, usability, and perceived usefulness were found to be obstacles to gaining trust [39,42,47,50,53,54,69,70].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [53]
The Further discussion (pp. 25, 27) links device-performance concerns, privacy breaches, psychological resistance, and doubts about clinical evidence to lack of trust in wearable medical devices and their providers. The Related studies section (p. 7) identifies perceived usefulness and ease of use as factors in acceptance, but does not establish them—or developers’ availability and usability—as obstacles to gaining trust. The paper discusses unsafe outcomes as a concern, but not every item in the citing sentence is supported as a trust barrier. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThere is no clarity in the area of regulatory structures with regard to which regulatory body should be consulted for AI developments and deployments.
“Other researchers noted that there was no clarity in the area of regulatory structures with regard to which regulatory body should be consulted for AI developments and deployments [36,53].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [53]
Section 4.5, p. 22, reports that existing legislation does not adequately clarify responsibility when accidents involve AI-enabled wearable medical devices. That supports a concern about legal clarity, but the paper does not say that developers lack clarity about which regulatory body to consult for AI development or deployment. (LLM, conf=0.82)
Experimental — accuracy not yet measuredFinancial barriers in the context of cost and benefits, the lack of a sustainable business model, and insufficient funding to meet public demands should also be considered.
“Sun and Medaglia [50] and Xing et al [53] reported that financial barriers in the context of cost and benefits, the lack of a sustainable business model, and insufficient funding to meet public demands should also be considered.”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [53]
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- Authors
- Xing, F., Peng, G., Zhang, B., Li, S., & Liang, X.
- Affiliations
- Sun Yat-sen University (CN)
- University of Sheffield (GB)
- Corresponding author
- Peng, Guochao
- Venue
- Technological Forecasting and Social Change
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 166
- Pages
- 120609
- ISSN
- 0040-1625 (ISSN-L)
- OpenAlex
- W3122718709
- Cited by
- 73
- References
- 62
- Citations by year
- 2026: 11 · 2025: 24 · 2024: 11 · 2023: 13 · 2022: 11 · 2021: 3
- FWCI
- 13.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Technological Forecasting and Social Change (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Guangdong Province
- Topics
- Social Sciences › Demography › Technology Use by Older Adults
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Health Sciences › Health Professions › General Health Professions › Social Policies and Healthcare Reform
- Keywords
- wearable medical devices, artificial intelligence, socio-technical barriers, population aging, large-scale deployment, Internet of Things, older adults, China, public-private collaboration, health technology adoption, legal and regulatory barriers, financial barriers
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