Surveying Public Perceptions of Artificial Intelligence in Health Care in the United States: Systematic Review
- PMID: 37014676
- PMCID: PMC10131909
- DOI: 10.2196/40337
Surveying Public Perceptions of Artificial Intelligence in Health Care in the United States: Systematic Review
Abstract
Background: This paper reviews nationally representative public opinion surveys on artificial intelligence (AI) in the United States, with a focus on areas related to health care. The potential health applications of AI continue to gain attention owing to their promise as well as challenges. For AI to fulfill its potential, it must not only be adopted by physicians and health providers but also by patients and other members of the public.
Objective: This study reviews the existing survey research on the United States' public attitudes toward AI in health care and reveals the challenges and opportunities for more effective and inclusive engagement on the use of AI in health settings.
Methods: We conducted a systematic review of public opinion surveys, reports, and peer-reviewed journal articles published on Web of Science, PubMed, and Roper iPoll between January 2010 and January 2022. We include studies that are nationally representative US public opinion surveys and include at least one or more questions about attitudes toward AI in health care contexts. Two members of the research team independently screened the included studies. The reviewers screened study titles, abstracts, and methods for Web of Science and PubMed search results. For the Roper iPoll search results, individual survey items were assessed for relevance to the AI health focus, and survey details were screened to determine a nationally representative US sample. We reported the descriptive statistics available for the relevant survey questions. In addition, we performed secondary analyses on 4 data sets to further explore the findings on attitudes across different demographic groups.
Results: This review includes 11 nationally representative surveys. The search identified 175 records, 39 of which were assessed for inclusion. Surveys include questions related to familiarity and experience with AI; applications, benefits, and risks of AI in health care settings; the use of AI in disease diagnosis, treatment, and robotic caregiving; and related issues of data privacy and surveillance. Although most Americans have heard of AI, they are less aware of its specific health applications. Americans anticipate that medicine is likely to benefit from advances in AI; however, the anticipated benefits vary depending on the type of application. Specific application goals, such as disease prediction, diagnosis, and treatment, matter for the attitudes toward AI in health care among Americans. Most Americans reported wanting control over their personal health data. The willingness to share personal health information largely depends on the institutional actor collecting the data and the intended use.
Conclusions: Americans in general report seeing health care as an area in which AI applications could be particularly beneficial. However, they have substantial levels of concern regarding specific applications, especially those in which AI is involved in decision-making and regarding the privacy of health information.
Keywords: AI; artificial intelligence; health care; public opinion; public perception.
©Becca Beets, Todd P Newman, Emily L Howell, Luye Bao, Shiyu Yang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.04.2023.
Conflict of interest statement
Conflicts of Interest: None declared.
Figures
Similar articles
-
Home treatment for mental health problems: a systematic review.Health Technol Assess. 2001;5(15):1-139. doi: 10.3310/hta5150. Health Technol Assess. 2001. PMID: 11532236
-
Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19.Cochrane Database Syst Rev. 2022 May 20;5(5):CD013665. doi: 10.1002/14651858.CD013665.pub3. Cochrane Database Syst Rev. 2022. PMID: 35593186 Free PMC article.
-
Eliciting adverse effects data from participants in clinical trials.Cochrane Database Syst Rev. 2018 Jan 16;1(1):MR000039. doi: 10.1002/14651858.MR000039.pub2. Cochrane Database Syst Rev. 2018. PMID: 29372930 Free PMC article.
-
Cost-effectiveness of using prognostic information to select women with breast cancer for adjuvant systemic therapy.Health Technol Assess. 2006 Sep;10(34):iii-iv, ix-xi, 1-204. doi: 10.3310/hta10340. Health Technol Assess. 2006. PMID: 16959170
-
Psychological interventions for adults who have sexually offended or are at risk of offending.Cochrane Database Syst Rev. 2012 Dec 12;12(12):CD007507. doi: 10.1002/14651858.CD007507.pub2. Cochrane Database Syst Rev. 2012. PMID: 23235646 Free PMC article.
Cited by
-
Using artificial intelligence to promote equitable care for inpatients with language barriers and complex medical needs: clinical stakeholder perspectives.J Am Med Inform Assoc. 2024 Feb 16;31(3):611-621. doi: 10.1093/jamia/ocad224. J Am Med Inform Assoc. 2024. PMID: 38099504 Free PMC article.
-
Population preferences for AI system features across eight different decision-making contexts.PLoS One. 2023 Dec 1;18(12):e0295277. doi: 10.1371/journal.pone.0295277. eCollection 2023. PLoS One. 2023. PMID: 38039320 Free PMC article.
-
A survey on practitioners' attitudes toward artificial intelligence in radiology.Croat Med J. 2023 Aug 31;64(4):289-291. doi: 10.3325/cmj.2023.64.289. Croat Med J. 2023. PMID: 37654041 Free PMC article. No abstract available.
-
Artificial Intelligence in Health Promotion and Disease Reduction: Rapid Review.J Med Internet Res. 2025 Aug 1;27:e70381. doi: 10.2196/70381. J Med Internet Res. 2025. PMID: 40788006 Free PMC article. Review.
-
Assessing Public Knowledge and Acceptance of Using Artificial Intelligence Doctors as a Partial Alternative to Human Doctors in Saudi Arabia: A Cross-Sectional Study.Cureus. 2024 Jul 13;16(7):e64461. doi: 10.7759/cureus.64461. eCollection 2024 Jul. Cureus. 2024. PMID: 39135842 Free PMC article.
References
-
- Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts HJ. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin. 2019 Mar 05;69(2):127–57. doi: 10.3322/caac.21552. https://europepmc.org/abstract/MED/30720861 - DOI - PMC - PubMed
-
- Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJ. Artificial intelligence in radiology. Nat Rev Cancer. 2018 Aug;18(8):500–10. doi: 10.1038/s41568-018-0016-5. https://europepmc.org/abstract/MED/29777175 10.1038/s41568-018-0016-5 - DOI - PMC - PubMed
-
- Kyrarini M, Lygerakis F, Rajavenkatanarayanan A, Sevastopoulos C, Nambiappan HR, Chaitanya KK, Babu AR, Mathew J, Makedon F. A survey of robots in healthcare. Technologies. 2021 Jan 18;9(1):8. doi: 10.3390/technologies9010008. - DOI
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources