Evaluating artificial intelligence applications in clinical settings
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Artificial intelligence (AI)-based systems have been shown to reliably recognize cardiovascular disease risk 1 and diagnose conditions such as diabetic retinopathy 2,3 and melanoma 4 from medical images.These advances in image-based medical diagnosis have been widely publicized in the media +
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AI has been reported to improve the assessment of medical imaging to detect cases such as malignancy and diabetic retinopathy.
“For instance, AI has been reported to improve the assessment of medical imaging to detect cases such as malignancy and diabetic retinopathy.30,31”
From Artificial intelligence: opportunities and implications for the health workforce (2020) · cites this paper as [31]
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Cites
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697 · see citing works
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Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0195-0 · Cited by 1,865 · see citing works
AI can be sexist and racist — it’s time to make it fair
Zou, J., & Schiebinger, L. (2018) · Nature · doi:10.1038/d41586-018-05707-8 · Cited by 765 · see citing works
Evaluation of Artificial Intelligence–Based Grading of Diabetic Retinopathy in Primary Care
Kanagasingam, Y., Xiao, D., Vignarajan, J., Preetham, A., Tay-Kearney, M.-L., & Mehrotra, A. (2018) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.2665 · Cited by 185 · see citing works
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Kim, D. W., Jang, H. Y., Kim, K. W., Shin, Y., & Park, S. H. (2019) · Korean Journal of Radiology · doi:10.3348/kjr.2019.0025 · Cited by 459 · see citing works
Evaluation framework to guide implementation of AI systems into healthcare settings
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What should medical students know about artificial intelligence in medicine?
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The AI life cycle: a holistic approach to creating ethical AI for health decisions
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Artificial intelligence for ultrasonography: unique opportunities and challenges
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- Authors
- Nsoesie, E. O.
- Affiliations
- University of Washington (US)
- Corresponding author
- Nsoesie, Elaine O.
- ORCID
- Nsoesie, Elaine O.
- Venue
- JAMA Network Open
- Publisher
- American Medical Association (AMA)
- Published
- 2018-09-28
- Volume
- 1
- Issue
- 5
- Pages
- e182658
- ISSN
- 2574-3805 (ISSN-L)
- OpenAlex
- W2893366129
- Cited by
- 48
- References
- 6
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 7 · 2023: 6 · 2022: 8 · 2021: 4 · 2020: 10 · 2019: 5
- FWCI
- 1.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JAMA Network Open (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- artificial intelligence, clinical applications of AI, cardiovascular risk prediction, diabetic retinopathy diagnosis, melanoma diagnosis, medical image analysis
- MeSH terms
- Artificial Intelligence, Diabetic Retinopathy, Humans, Primary Health Care, Neural Networks, Computer
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