The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare
Abstract
INTRODUCTION: Artificial intelligence (AI) and machine learning (ML) are rapidly evolving fields in various sectors, including healthcare. This article reviews AI's present applications in healthcare, including its benefits, limitations and future scope. SOURCES OF DATA: A review of the English literature was conducted with search terms 'AI' or 'ML' or 'deep learning' and 'healthcare' or 'medicine' using PubMED and Google Scholar from 2000-2021. AREAS OF AGREEMENT: AI could transform physician workflow and patient care through its applications, from assisting physicians and replacing administrative tasks to augmenting medical knowledge. AREAS OF CONTROVERSY: From challenges training ML systems to unclear accountability, AI's implementation is difficult and incremental at best. Physicians also lack understanding of what AI implementation could represent. GROWING POINTS: AI can ultimately prove beneficial in healthcare, but requires meticulous governance similar to the governance of physician conduct. AREAS TIMELY FOR DEVELOPING RESEARCH: Regulatory guidelines are needed on how to safely implement and assess AI technology, alongside further research into the specific capabilities and limitations of its medical use.
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In the citing paper
“In the field of clinical decision making, AI has demonstrated remarkable capabilities for predicting and classifying diagnoses, as well as providing recommendations and insights.4,7”
From Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews (2024) · cites this paper as [7]
In the citing paper
“Therefore, collaboration between institutions or even at the national level is crucial to allow for separate datasets for comparison [49].”
From Artificial Intelligence Revolutionizing the Field of Medical Education (2023) · cites this paper as [49]
In the citing paper
“The use of an external dataset can validate results and reduce bias.”
From Artificial Intelligence Revolutionizing the Field of Medical Education (2023) · cites this paper as [49]
In the citing paper
“However, this social fear of AI rendering teams and educators obsolete is a misunderstanding based on popular science fiction beliefs [49].”
From Artificial Intelligence Revolutionizing the Field of Medical Education (2023) · cites this paper as [49]
Citations
Cites
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837
International evaluation of an AI system for breast cancer screening
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1799-6 · Cited by 3,287
Ting, D. S. W., Cheung, C. Y.-L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I. Y., Lee, S. Y., et al. (2017) · JAMA · doi:10.1001/jama.2017.18152 · Cited by 2,402
Self-citationLiu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30123-2 · Cited by 1,883
Lakhani, P., & Sundaram, B. (2017) · Radiology · doi:10.1148/radiol.2017162326 · Cited by 1,775
Attia, Z. I., Noseworthy, P. A., Lopez-Jimenez, F., Asirvatham, S. J., Deshmukh, A. J., Gersh, B. J., Carter, R. E., Yao, X., Rabinstein, A. A., Erickson, B. J., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(19)31721-0 · Cited by 1,613
Artificial intelligence in cardiology
Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., Ashley, E., & Dudley, J. T. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.03.521 · Cited by 1,202
Artificial intelligence in medicine
Ramesh, A., Kambhampati, C., Monson, J. Q., & Drew, P. J. (2004) · Annals of The Royal College of Surgeons of England · doi:10.1308/147870804290 · Cited by 1,095
Sun, T. Q., & Medaglia, R. (2019) · Government Information Quarterly · doi:10.1016/j.giq.2018.09.008 · Cited by 948
Flagged first, then by citations
Research works printed but not linked: 3
- Eggers, 2017, AI-augmented government: Using cognitive technologies to redesign public sector work A report from the Deloitte Center for Government Insights
- Google, 2019, AI Explainability Whitepaper, 1
- GOV.UK, 2019, Initial code of conduct for data-driven health and care technology
Cited by
Sorted by citations
What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature
Lo, C. K. (2023) · Education Sciences · doi:10.3390/educsci13040410 · Cited by 2,024
Regulating ChatGPT and other Large Generative AI Models
Hacker, P., Engel, A., & Mauer, M. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594067 · Cited by 466
“However, errors will be costly, and risks ranging from discrimination and privacy to disrespectful content need to be adequately addressed [4-6].”
BackgroundPalaniappan, K., Lin, E. Y. T., & Vogel, S. (2024) · Healthcare · doi:10.3390/healthcare12050562 · Cited by 301
Khosravi, M., Zare, Z., Mojtabaeian, S. M., & Izadi, R. (2024) · Health Services Research and Managerial Epidemiology · doi:10.1177/23333928241234863 · Cited by 265
Randomized Clinical Trials of Machine Learning Interventions in Health Care: A Systematic Review
Plana, D., Shung, D. L., Grimshaw, A. A., Saraf, A., Sung, J. J. Y., & Kann, B. H. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.33946 · Cited by 236
Metadata
- Authors
- Aung, Y. Y. M., Wong, D. C. S., & Ting, D. S. W.
- Affiliations
- Imperial College London (GB)
- University of Cambridge (GB)
- National University of Singapore (SG)
- Corresponding author
- Ting, Daniel S W
- Venue
- British Medical Bulletin
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-08-17
- Volume
- 139
- Issue
- 1
- Pages
- 4-15
- ISSN
- 0007-1420 (ISSN-L)
- 1471-8391
- OpenAlex
- W3196012986
- Cited by
- 511
- References
- 46
- Citations by year
- 2026: 111 · 2025: 161 · 2024: 131 · 2023: 83 · 2022: 22 · 2021: 3
- FWCI
- 4.86
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- British Medical Bulletin (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence in healthcare, machine learning, deep learning, physician workflow, administrative automation, AI implementation challenges, accountability, AI governance, regulatory guidelines, AI safety, AI limitations
- MeSH terms
- Machine Learning, Artificial Intelligence, Delivery of Health Care, Humans, Medicine
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