Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review
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Abstract
Background: Artificial intelligence (AI) has the potential to improve the efficiency and effectiveness of health care service delivery. However, the perceptions and needs of such systems remain elusive, hindering efforts to promote AI adoption in health care. Objective: This study aims to provide an overview of the perceptions and needs of AI to increase its adoption in health care. Methods: A systematic scoping review was conducted according to the 5-stage framework by Arksey and O’Malley. Articles that described the perceptions and needs of AI in health care were searched across nine databases: ACM Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science for studies that were published from inception until June 21, 2021. Articles that were not specific to AI, not research studies, and not written in English were omitted. Results: Of the 3666 articles retrieved, 26 (0.71%) were eligible and included in this review. The mean age of the participants ranged from 30 to 72.6 years, the proportion of men ranged from 0% to 73.4%, and the sample sizes for primary studies ranged from 11 to 2780. The perceptions and needs of various populations in the use of AI were identified for general, primary, and community health care; chronic diseases self-management and self-diagnosis; mental health; and diagnostic procedures. The use of AI was perceived to be positive because of its availability, ease of use, and potential to improve efficiency and reduce the cost of health care service delivery. However, concerns were raised regarding the lack of trust in data privacy, patient safety, technological maturity, and the possibility of full automation. Suggestions for improving the adoption of AI in health care were highlighted: enhancing personalization and customizability; enhancing empathy and personification of AI-enabled chatbots and avatars; enhancing user experience, design, and interconnectedness with other devices; and educating the public on AI capabilities. Several corresponding mitigation strategies were also identified in this study. Conclusions: The perceptions and needs of AI in its use in health care are crucial in improving its adoption by various stakeholders. Future studies and implementations should consider the points highlighted in this study to enhance the acceptability and adoption of AI in health care. This would facilitate an increase in the effectiveness and efficiency of health care service delivery to improve patient outcomes and satisfaction.
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A previous scoping review found that 81% of studies reported healthcare professionals had concerns about AI systems, particularly regarding patient safety.
“A previous scoping review found that 81% of studies reported healthcare professionals had concerns about AI systems, particularly regarding patient safety [3].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [3]
The Results section (Concerns and Mitigation Strategies, Patient Safety) reports that 21 of 26 studies (81%) discussed respondents’ lack of trust in AI to ensure patient safety. It does not identify those respondents as healthcare professionals; it says most responses represented the public, including patients (66.6%). (LLM, conf=0.90)
Experimental — accuracy not yet measuredPrevious reviews of AI in healthcare found that AI significantly enhanced accuracy and efficiency in medical processes, particularly in diagnosis and treatment.
“Previous reviews of AI in healthcare have found AI significantly enhanced accuracy and efficiency in medical processes, particularly in diagnosis and treatment, potentially improving patient safety and reducing healthcare costs. [2,3]”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [3]
The Results (Positive Perceptions; page not provided) report that studies highlighted AI’s potential to increase the speed and accuracy of processes including diagnosis. The review primarily examines perceptions and does not establish that AI significantly enhanced accuracy and efficiency in diagnosis and treatment; the claim turns potential benefits into demonstrated results. (LLM, conf=0.87)
Experimental — accuracy not yet measuredConcerns related to privacy, trust, transparency, and potential decision errors impacting patient safety have been raised by health professionals, hindering acceptance and adoption of AI in practice.
“Despite this, concerns related to privacy, trust, transparency, and potential decision errors impacting patient safety have been raised by health professionals, hindering their acceptance and adoption in practice [2,3].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [3]
The Results (Concerns and Mitigation Strategies; page unavailable) report concerns about data privacy, trust, transparency, and medical errors affecting patient safety. Health care providers were among the respondents, but the review says most patient-safety responses came from the public, including patients. Its Conclusions link addressing these concerns to improving adoption; they do not establish that the listed concerns specifically hindered health professionals’ acceptance in practice. (LLM, conf=0.82)
Experimental — accuracy not yet measuredHuman trust is an important factor, and further education on AI and transparency information can build this trust for clinicians and patients.
“Human trust is an important factor, and further education on AI and transparency information can build this trust for clinicians and patients [59–61].”
From Use of artificial intelligence in critical care: opportunities and obstacles (2024) · cites this paper as [61]
The Results (Concerns and Mitigation Strategies; page unavailable) identify lack of trust and suggest transparency about data use, AI reasoning, and accuracy as mitigation strategies. The Results (Needs to Improve Adoption of AI in Health Care; page unavailable) identify a need to educate the public and clinicians about AI, but link better understanding to expectations and service delivery rather than explicitly to increased trust in both groups. (LLM, conf=0.78)
Experimental — accuracy not yet measuredPrior reviews of AI found that trust and transparency can impact AI adoption in healthcare.
“As previously mentioned, prior reviews of AI have found trust and transparency can impact AI adoption in healthcare [2,3].”
From Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals’ perspectives (2026) · cites this paper as [3]
Integrating AI is hindered by fears, missing sensitization, and a lack of data.
“Integrating AI is hindered by fears, missing sensitization, and a lack of data [8, 22, 23, 25, 27, 30].”
From Nurses’ perceptions, experience and knowledge regarding artificial intelligence: results from a cross-sectional online survey in Germany (2024) · cites this paper as [27]
Concerns include unawareness, mistrust, anxiety, worries about the patient relationship and job replacement.
“Concerns include unawareness, mistrust, anxiety, worries about the patient relationship and job replacement [5, 8, 10, 13, 25–27].”
From Nurses’ perceptions, experience and knowledge regarding artificial intelligence: results from a cross-sectional online survey in Germany (2024) · cites this paper as [27]
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- Authors
- Chew, H. S. J., & Achananuparp, P.
- Affiliations
- National University of Singapore (SG)
- Corresponding author
- Chew, Han Shi Jocelyn
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2022-01-14
- Volume
- 24
- Issue
- 1
- Pages
- e32939
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC8800095
- OpenAlex
- W4206636637
- Cited by
- 243
- References
- 62
- Citations by year
- 2026: 66 · 2025: 82 · 2024: 54 · 2023: 26 · 2022: 15
- FWCI
- 3.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Research Foundation
- National Research Foundation Singapore
- National University Health System
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in healthcare, artificial intelligence adoption, user perception, patient needs, trust in AI, data privacy, patient safety, chatbots, avatars, chronic disease self-management, mental health, diagnostic procedures, health care delivery
- MeSH terms
- Adult, Aged, Artificial Intelligence, Chronic Disease, Community Health Services, Delivery of Health Care, Humans, Male, Mental Health, Middle Aged
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