Deep Learning in Medicine—Promise, Progress, and Challenges
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OHDSI has consolidated 1.26 billion patient records from 17 different countries.
“There are various international efforts in gathering EMR data across countries including Observational Health Data Science and Informatics (OHDSI), who have consolidated 1.26 billion patient records from 17 different countries [40].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [40]
Modern deep learning models feature millions of parameters that need to be learned from sufficiently large curated data sets in order to achieve clinical-grade accuracy, while being safe, fair, equitable and generalising well to unseen data.
“Modern DL models feature millions of parameters that need to be learned from sufficiently large curated data sets in order to achieve clinical-grade accuracy, while being safe, fair, equitable and generalising well to unseen data2–5.”
From The future of digital health with federated learning (2020) · cites this paper as [2]
Despite the advantages of federated learning, it does not solve all issues inherent to learning on medical data; successful model training still depends on factors like data quality, bias and standardisation.
“Despite the advantages of FL, it does not solve all issues that are inherent to learning on medical data.”
From The future of digital health with federated learning (2020) · cites this paper as [2]
Suboptimal quality of clinical data often limits the performance of machine learning algorithms.
“Suboptimal quality of clinical data often limits the performance of ML algorithms (6,7).”
From Mitigating Bias in Radiology Machine Learning: 1. Data Handling (2022) · cites this paper as [6]
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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
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Big Data and Machine Learning in Health Care
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Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data
Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.3763 · Cited by 1,395 · see citing works
Deep Learning—A Technology With the Potential to Transform Health Care
Hinton, G. (2018) · JAMA · doi:10.1001/jama.2018.11100 · Cited by 667 · see citing works
Clinical Implications and Challenges of Artificial Intelligence and Deep Learning
Stead, W. W. (2018) · JAMA · doi:10.1001/jama.2018.11029 · Cited by 250 · see citing works
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The future of digital health with federated learning
Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00323-1 · Cited by 3,119 · see citing works
The rise of artificial intelligence in healthcare applications
Bohr, A., & Memarzadeh, K. (2020) · Artificial Intelligence in Healthcare · doi:10.1016/b978-0-12-818438-7.00002-2 · Cited by 1,636 · see citing works
Artificial intelligence for clinical trial design
Harrer, S., Shah, P., Antony, B., & Hu, J. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.05.005 · Cited by 672 · see citing works
Aung, Y. Y. M., Wong, D. C. S., & Ting, D. S. W. (2021) · British Medical Bulletin · doi:10.1093/bmb/ldab016 · Cited by 511 · see citing works
AI in Health: State of the Art, Challenges, and Future Directions
Wang, F., & Preininger, A. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677908 · Cited by 398 · see citing works
Self-citationSendak, M. P., Ratliff, W., Sarro, D., Alderton, E., Futoma, J., Gao, M., Nichols, M., Revoir, M., Yashar, F., Miller, C., et al. (2020) · JMIR Medical Informatics · doi:10.2196/15182 · Cited by 287 · see citing works
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 · see citing works
Si, Y., Du, J., Li, Z., Jiang, X., Miller, T., Wang, F., Jim Zheng, W., & Roberts, K. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103671 · Cited by 235 · see citing works
Self-citationRandomized 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 · see citing works
Health Care Students’ Perspectives on Artificial Intelligence: Countrywide Survey in Canada
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Mitigating Bias in Radiology Machine Learning: 1. Data Handling
Rouzrokh, P., Khosravi, B., Faghani, S., Moassefi, M., Vera Garcia, D. V., Singh, Y., Zhang, K., Conte, G. M., & Erickson, B. J. (2022) · Radiology: Artificial Intelligence · doi:10.1148/ryai.210290 · Cited by 145 · see citing works
Recent advances in biomedical literature mining
Zhao, S., Su, C., Lu, Z., & Wang, F. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa057 · Cited by 134 · see citing works
Self-citationFinding missed cases of familial hypercholesterolemia in health systems using machine learning
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“25,26 In summary, the use of a classi fi er to detect putative cases of FH from the EHR allows the identi fi cation of patients who have a substantial probability of having the condition.”
BackgroundLarge language models streamline automated machine learning for clinical studies
Tayebi Arasteh, S., Han, T., Lotfinia, M., Kuhl, C., Kather, J. N., Truhn, D., & Nebelung, S. (2024) · Nature Communications · doi:10.1038/s41467-024-45879-8 · Cited by 114 · see citing works
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“69 Additionally, medical data have become increasingly available in digital format due to technological advancements and public policy initiatives.”
BackgroundTrust in AI: why we should be designing for APPROPRIATE reliance
Benda, N. C., Novak, L. L., Reale, C., & Ancker, J. S. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab238 · Cited by 76 · see citing works
Nam, J. G., Park, S., Park, C. M., Jeon, Y. K., Chung, D. H., Goo, J. M., Kim, Y. T., & Kim, H. (2022) · Radiology · doi:10.1148/radiol.213262 · Cited by 32 · see citing works
Machine learning’s feet of clay.
Kriston, L. (2020) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.13191 · Cited by 10 · see citing works
Tokgöz, P., Hafner, J., & Dockweiler, C. (2023) · Das Gesundheitswesen · doi:10.1055/a-2098-3108 · Cited by 5 · see citing works
- Authors
- Wang, F., Casalino, L. P., & Khullar, D.
- Affiliations
- Weill Cornell Medicine (US)
- Venue
- JAMA Internal Medicine
- Publisher
- American Medical Association (AMA)
- Published
- 2019-03-01
- Volume
- 179
- Issue
- 3
- Pages
- 293
- ISSN
- 2168-6106 (ISSN-L)
- OpenAlex
- W2905483812
- Cited by
- 483
- References
- 6
- Citations by year
- 2026: 52 · 2025: 66 · 2024: 100 · 2023: 84 · 2022: 77 · 2021: 44 · 2020: 45 · 2019: 15
- FWCI
- 7.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- JAMA Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- deep learning, artificial intelligence in healthcare, clinical application, AI adoption barriers, diagnostic imaging, clinical decision support
- MeSH terms
- Deep Learning, Decision Making, Computer-Assisted, Humans
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