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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AI has demonstrated capabilities for predicting and classifying diagnoses and providing recommendations and insights in clinical decision making.
“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]
Collaboration between institutions or even at the national level is crucial to allow for separate datasets for comparison.
“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]
The use of an external dataset can validate results and reduce bias.
“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]
The social fear of AI rendering teams and educators obsolete is a misunderstanding based on popular science fiction beliefs.
“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]
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Challenges and opportunities of artificial intelligence in African health space
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Leveraging artificial intelligence to meet the sustainable development goals
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The medical profession transformed by artificial intelligence: Qualitative study
Mosch, L. K., Fürstenau, D., Brandt, J., Wagnitz, J., Klopfenstein, S. A. I., Poncette, A.-S., & Balzer, F. (2022) · Digital Health · doi:10.1177/20552076221143903 · Cited by 42 · see citing works
The use of artificial intelligence in the treatment of rare diseases: A scoping review
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Lymph node metastasis in cancer: Clearing the clouds to see the dawn
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RetractedA rapid review of machine learning approaches for telemedicine in the scope of COVID-19
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Ready but irresponsible? Analysis of the Government Artificial Intelligence Readiness Index
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The impact of artificial intelligence on the tasks of mental healthcare workers: A scoping review
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State-of-the-Art in Responsible, Explainable, and Fair AI for Medical Image Analysis
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Current and future roles of artificial intelligence in retinopathy of prematurity
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Artificial Intelligence in Healthcare: Current Regulatory Landscape and Future Directions
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Patient views on the implementation of artificial intelligence in radiotherapy
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Next Generation ECG: The Impact of Artificial Intelligence and Machine Learning
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“The use of AI would relieve the burden on health care professionals and increase the quality of work performed by reducing the possibility of errors and increasing accuracy (Aung et al., 2021).”
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Artificial Intelligence and medical specialties: support or substitution?
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Advances of Genome Editing with CRISPR/Cas9 in Neurodegeneration: The Right Path towards Therapy
Klinkovskij, A., Shepelev, M. V., Isaakyan, Y. A., Aniskin, D., & Ulasov, I. V. (2023) · Biomedicines · doi:10.3390/biomedicines11123333 · Cited by 23 · see citing works
- 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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