The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Medical Student Perspectives on the Impact of Artificial Intelligence on the Practice of Medicine
Abstract
Introduction Concerns about radiologists being replaced by artificial intelligence (AI) from the lay media could have a negative impact on medical students' perceptions of radiology as a viable specialty. The purpose of this study was to evaluate United States of America medical students' perceptions about radiology and other medical specialties in relation to AI.Methods An anonymous, web-based survey was sent to 32 radiology interest groups at United States medical schools. The survey was comprised of 6 questions assessing medical student perceptions of AI and its potential impact on radiology and other medical specialties. Responses were voluntary and collected over a 6-month period from November 2017 to April 2018.Results A total of 156 students responded with representation from each year of medical school. Over 75% agreed that AI would have a significant role in the future of medicine. Most (66%) agreed that diagnostic radiology would be the specialty most greatly affected. Nearly half (44%) reported that AI made them less enthusiastic about radiology. The majority of students (57%) obtained their information about AI from online articles. Thematic analysis of free answer comments revealed mostly neutral comments towards AI, however, the negative responses were the strongest and most detailed.Conclusions US medical students believe that AI will play a significant role in medicine, particularly in radiology. However, nearly half are less enthusiastic about the field of radiology due to AI. As the majority receive information about AI from online articles, which may have negative sentiments towards AI's impact on radiology, formal AI education and medical student outreach may help combat misinformation and help prevent the dissuading of medical students who might otherwise consider the specialty.
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“Additionally, students were apprehensive about GenAI, which may hinder critical thinking and creativity, and the impact of GenAI on job prospects (Ghotbi et al., 2022; Gong et al., 2019; Park et al., 2020) and human values (Gillissen et al., 2022; Jha et al., 2022).”
From Students’ voices on generative AI: perceptions, benefits, and challenges in higher education (2023) · cites this paper as Park et al., 2020
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“The mean respondent years of experience working with AI was only reported in three studies [7,21,28].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [21]
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“One study met the NOS criteria for good [17] and 22 studies met the NOS criteria for fair [7,8,18–36,37].”
From An evidence-based approach to artificial intelligence education for medical students: A systematic review (2023) · cites this paper as [21]
Citations
Cites
Medical students' attitude towards artificial intelligence: a multicentre survey
Pinto dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., Maintz, D., & Baeßler, B. (2019) · European Radiology · doi:10.1007/s00330-018-5601-1 · Cited by 754
Gong, B., Nugent, J. P., Guest, W., Parker, W., Chang, P. J., Khosa, F., & Nicolaou, S. (2019) · Academic Radiology · doi:10.1016/j.acra.2018.10.007 · Cited by 325
Reimagining Medical Education in the Age of AI
(2019) · AMA Journal of Ethics · doi:10.1001/amajethics.2019.146 · Cited by 263
Adoption of electronic health records and barriers
Palabindala, V., Pamarthy, A., & Jonnalagadda, N. (2016) · Journal of Community Hospital Internal Medicine Perspectives · doi:10.3402/jchimp.v6.32643 · Cited by 74
The day when computers read between lines
Yamada, K., & Mori, S. (2019) · Japanese Journal of Radiology · doi:10.1007/s11604-019-00833-3 · Cited by 7
Cardiac surgery: What the future holds?
Parissis, H. (2011) · Journal of Cardiothoracic Surgery · doi:10.1186/1749-8090-6-93 · Cited by 7
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Students’ voices on generative AI: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00411-8 · Cited by 2,301
A scoping review of artificial intelligence in medical education: BEME Guide No. 84
Gordon, M., Daniel, M., Ajiboye, A., Uraiby, H., Xu, N. Y., Bartlett, R., Hanson, J., Haas, M., Spadafore, M., Grafton-Clarke, C., et al. (2024) · Medical Teacher · doi:10.1080/0142159x.2024.2314198 · Cited by 679
Chen, M., Zhang, B., Cai, Z., Seery, S., Gonzalez, M. J., Ali, N. M., Ren, R., Qiao, Y., Xue, P., & Jiang, Y. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.990604 · Cited by 258
Mousavi Baigi, S. F., Sarbaz, M., Ghaddaripouri, K., Ghaddaripouri, M., Mousavi, A. S., & Kimiafar, K. (2023) · Health Science Reports · doi:10.1002/hsr2.1138 · Cited by 255
Hogg, H. D. J., Al-Zubaidy, M., Technology Enhanced Macular Services Study Reference Group, Talks, J., Denniston, A. K., Kelly, C. J., Malawana, J., Papoutsi, C., Teare, M. D., Keane, P. A., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/39742 · Cited by 163
Metadata
- Authors
- Park, C. J., Yi, P. H., & Siegel, E. L.
- Affiliations
- Pennsylvania State University (US)
- Johns Hopkins University (US)
- University of Maryland Medical Center (US)
- Corresponding author
- Park, Christian J.
- Venue
- Current Problems in Diagnostic Radiology
- Publisher
- Elsevier BV
- Published
- 2021-09
- Volume
- 50
- Issue
- 5
- Pages
- 614-619
- ISSN
- 0363-0188 (ISSN-L)
- OpenAlex
- W3037873741
- Cited by
- 167
- References
- 6
- Citations by year
- 2026: 27 · 2025: 35 · 2024: 24 · 2023: 33 · 2022: 20 · 2021: 24
- FWCI
- 5.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Problems in Diagnostic Radiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Social Sciences › Gender Studies › Diversity and Career in Medicine
- Keywords
- artificial intelligence, medical students, perceptions of AI, radiology workforce, specialty choice, artificial intelligence in education, misinformation
- MeSH terms
- Radiologists, Artificial Intelligence, Humans, Radiography, Radiology, Students, Medical, United States
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