Artificial intelligence (ai) systems for interpreting complex medical datasets
Abstract
Advances in machine intelligence have created powerful capabilities in algorithms that find hidden patterns in data, classify objects based on their measured characteristics, and associate similar patients/diseases/drugs based on common features. However, artificial intelligence (AI) applications in medical data have several technical challenges: complex and heterogeneous datasets, noisy medical datasets, and explaining their output to users. There are also social challenges related to intellectual property, data provenance, regulatory issues, economics, and liability.
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The requirement of a large amount of labeled data is a big problem in solving critical domain-specific tasks, specifically medical applications, where making large-scale, high-quality annotated medical datasets is very complex and expensive.
“So, the requirement of a large amount of labeled data is a big problem in solving some critical domain-specific tasks, specifically the applications for the medical domain, where the making of large-scale, high-quality annotated medical datasets is very complex, and expensive [40].”
From A survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic (2020) · cites this paper as [40]
Cites
Denoising genome-wide histone ChIP-seq with convolutional neural networks
Koh, P. W., Pierson, E., & Kundaje, A. B. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx243 · Cited by 59 · see citing works
Yu, K., Liu, X., Qi, T., Yang, H., Whitfield, D. M., Chen, Q. Y., Huisman, E. J. C., & Hu, C. (2016) · Nature Communications · doi:10.1038/ncomms12223 · Cited by 57 · see citing works
Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomesPreprint
Artemov, A. V., Lane, E., Vanhaelen, Q., Aliper, A. M., Ozerov, I. V., & Zhavoronkov, A. A. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/095653 · Cited by 41 · see citing works
Impact of regulatory variation across human iPSCs and differentiated cellsPreprint
Banovich, N. E., Li, Y., Raj, A., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/091660 · Cited by 20 · see citing works
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Koh, P. W., Pierson, E., & Kundaje, A. B. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/052118 · Cited by 11 · see citing works
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Artificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817 · see citing works
Current Applications and Future Impact of Machine Learning in Radiology
Choy, G., Khalilzadeh, O., Michalski, M., Do, S., Samir, A. E., Pianykh, O. S., Geis, J. R., Pandharipande, P. V., Brink, J. A., & Dreyer, K. J. (2018) · Radiology · doi:10.1148/radiol.2018171820 · Cited by 800 · see citing works
A survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic
Sufian, A., Ghosh, A., Sadiq, A. S., & Smarandache, F. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101830 · Cited by 156 · see citing works
- Authors
- Altman, R.
- Affiliations
- Stanford University Stanford California USA
- Venue
- Clinical Pharmacology & Therapeutics
- Publisher
- Wiley
- Published
- 2017-03-17
- Volume
- 101
- Issue
- 5
- Pages
- 585-586
- ISSN
- 0009-9236 (ISSN-L)
- 1532-6535
- OpenAlex
- W2586515148
- Cited by
- 65
- References
- 5
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 8 · 2023: 3 · 2022: 7 · 2021: 8 · 2020: 12 · 2019: 6 · 2018: 8 · 2017: 4
- FWCI
- 4.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Clinical Pharmacology & Therapeutics (publishedVersion)
- 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 in healthcare, explainable AI, intellectual property, data provenance, regulatory challenges, liability, drug repurposing, patient classification
- MeSH terms
- Algorithms, Artificial Intelligence, Data Interpretation, Statistical, Humans, Databases, Factual
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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