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Cited by 100Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIArtificial Intelligence in Healthcare

Implementation of artificial intelligence in medicine: status analysis and development suggestions

Xiang, Yifan, Zhao, Lanqin, Liu, Zhenzhen, Wu, Xiaohang, Chen, Jingjing, Long, Erping, Lin, Duoru, Zhu, Yi +3 more·2020·Artificial Intelligence in Medicine
DOI 10.1016/j.artmed.2019.101780
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Abstract

The general public's attitudes, demands, and expectations regarding medical AI could provide guidance for the future development of medical AI to satisfy the increasing needs of doctors and patients. The objective of this study is to investigate public perceptions, receptivity, and demands regarding the implementation of medical AI. An online questionnaire was designed to investigate the perceptions, receptivity, and demands of general public regarding medical AI between October 13 and October 30, 2018. The distributions of the current achievements, public perceptions, receptivity, and demands among individuals in different lines of work (i.e., healthcare vs non-healthcare) and different age groups were assessed by performing descriptive statistics. The factors associated with public receptivity of medical AI were assessed using a linear regression model. In total, 2,780 participants from 22 provinces were enrolled. Healthcare workers accounted for 54.3 % of all participants. There was no significant difference between the healthcare workers and non-healthcare workers in the high proportion (99 %) of participants expressing acceptance of AI (p = 0.8568), but remarkable distributional differences were observed in demands (p < 0.001 for both demands for AI assistance and the desire for AI improvements) and perceptions (p < 0.001 for safety, validity, trust, and expectations). High levels of receptivity (approximately 100 %), demands (approximately 80 %), and expectations (100 %) were expressed among different age groups. The receptivity of medical AI among the non-healthcare workers was associated with gender, educational qualifications, and demands and perceptions of AI. There was a very large gap between current availability of and public demands for intelligence services (p < 0.001). More than 90 % of healthcare workers expressed a willingness to devote time to learning about AI and participating in AI research. The public exhibits a high level of receptivity regarding the implementation of medical AI. To date, the achievements have been rewarding, and further advancements are required to satisfy public demands. There is a strong demand for intelligent assistance in many medical areas, including imaging and pathology departments, outpatient services, and surgery. More contributions are imperative to facilitate integrated and advantageous implementation in medical AI.

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Authors
Xiang, Y., Zhao, L., Liu, Z., Wu, X., Chen, J., Long, E., Lin, D., Zhu, Y., Chen, C., Lin, Z., & Lin, H.
Affiliations
  • Sun Yat-sen University (CN)
  • University of Miami (US)
Corresponding author
  • Lin, Duoru
  • Lin, Zhuoling
  • Lin, Haotian
ORCID
Venue
Artificial Intelligence in Medicine
Publisher
Elsevier BV
Published
2020-01
Volume
102
Pages
101780
ISSN
0933-3657 (ISSN-L)
DOI
10.1016/j.artmed.2019.101780
OpenAlex
W2994731224
Cited by
100
References
41
Citations by year
2026: 13 · 2025: 15 · 2024: 19 · 2023: 23 · 2022: 19 · 2021: 7 · 2020: 3 · 2013: 1
FWCI
2.30
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Artificial Intelligence in Medicine (publishedVersion)
  • PubMed (publishedVersion)
Funders
Ministry of Science and Technology of the People's Republic of China
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
artificial intelligence in healthcare, public perception of AI, public acceptance of AI, healthcare workers, non-healthcare workers, age differences, trust in AI
MeSH terms
Adult, Age Factors, Artificial Intelligence, Educational Status, Female, Health Personnel, Humans, Male, Medicine, Middle Aged, Physicians, Public Opinion, Surveys and Questionnaires, Regression Analysis, Sex Factors, Socioeconomic Factors

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