The DOI matches its Crossref record; 11 of the 12 references it cites were found in a registry or an index.
Big data and black-box medical algorithms
Open accessAbstract
New machine-learning techniques entering medicine present challenges in validation, regulation, and integration into practice.
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“For example, the kernels or intermediate features of a trained neural network may shed light on the learned structure in different layers of the network, giving rise to methods like class activation mapping (CAM).67”
From Addressing bias in big data and AI for health care: A call for open science (2021) · cites this paper as [67]
In the citing paper
“We propose that another important barrier to adoption of medical AI is its perceived opacity, that it is a 'black box' 13,14 .”
From Understanding, explaining, and utilizing medical artificial intelligence (2021) · cites this paper as [13]
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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
Predicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652
How the machine “thinks”: Understanding opacity in machine learning algorithms
Burrell, J. (2016) · Big Data & Society · doi:10.1177/2053951715622512 · Cited by 3,196 · cited 1×
“Some techniques do not identify any explicit relationships; for example, a neural network trained to identify tumors can identify them but uses opaque hidden layers to do so ( 6 ).”
BackgroundExternal validity of randomised controlled trials: “To whom do the results of this trial apply?”
Rothwell, P. M. (2005) · The Lancet · doi:10.1016/s0140-6736(04)17670-8 · Cited by 2,544
Real-World Evidence — What Is It and What Can It Tell Us?
Sherman, R. E., Anderson, S. A., Pan, G. J. D., Gray, G. W., Gross, T. P., Hunter, N. L., LaVange, L. M., Marinac‐Dabic, D., Marks, P. W., Robb, M. A., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmsb1609216 · Cited by 2,192
New FDA Breakthrough-Drug Category — Implications for Patients
Darrow, J. J., Avorn, J., & Kesselheim, A. S. (2014) · New England Journal of Medicine · doi:10.1056/nejmhle1311493 · Cited by 119
Liu, N. T., Holcomb, J. B., Wade, C. E., Batchinsky, A. I., Cancio, L. C., Darrah, M. I., & Salinas, J. (2013) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-013-1130-x · Cited by 67
Price, W. (2017) · Michigan Law Review · doi:10.36644/mlr.116.3.regulating · Cited by 64
Self-citationLiehr, T., Acquarola, N., Pyle, K., St‐Pierre, S., Rinholm, M., Bar, O., Wilhelm, K., & Schreyer, I. (2017) · Clinical Genetics · doi:10.1111/cge.13087 · Cited by 48
Price, & Nicholson, W. T. (2015) · Harvard journal of law & technology · Cited by 25
Cited by
Sorted by citations
Addressing bias in big data and AI for health care: A call for open science
Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021) · Patterns · doi:10.1016/j.patter.2021.100347 · Cited by 903
Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare
Cirillo, D., Catuara-Solarz, S., Morey, C., Guney, E., Subirats, L., Mellino, S., Gigante, A., Valencia, A., Rementeria, M. J., Chadha, A. S., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0288-5 · Cited by 553
Understanding, explaining, and utilizing medical artificial intelligence
Cadario, R., Longoni, C., & Morewedge, C. K. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01146-0 · Cited by 342
Saillard, C., Schmauch, B., Laifa, O., Moarii, M., Toldo, S., Zaslavskiy, M., Pronier, E., Laurent, A., Amaddeo, G., Regnault, H., et al. (2020) · Hepatology · doi:10.1002/hep.31207 · Cited by 332
Burgos-Artizzu, X. P., Coronado-Gutiérrez, D., Valenzuela-Alcaraz, B., Bonet-Carne, E., Eixarch, E., Crispi, F., & Gratacós, E. (2020) · Scientific Reports · doi:10.1038/s41598-020-67076-5 · Cited by 240
Metadata
- Authors
- Price, W. N.
- Affiliations
- University of Copenhagen (DK)
- Corresponding author
- Price, W. Nicholson
- ORCID
- Price, W. Nicholson
- Venue
- Science Translational Medicine
- Published
- 2018-12-12
- Volume
- 10
- Issue
- 471
- ISSN
- 1946-6234 (ISSN-L)
- 1946-6242
- PMC
- PMC6345162
- OpenAlex
- W2905322176
- Cited by
- 254
- References
- 12
- Citations by year
- 2026: 30 · 2025: 36 · 2024: 46 · 2023: 29 · 2022: 38 · 2021: 46 · 2020: 17 · 2019: 12
- FWCI
- 5.08
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science Translational Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (1-R01-CA-214829-01-A1)
- Novo Nordisk Fonden (NNF17SA0027784)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- big data, black-box algorithms, machine learning in medicine, algorithm validation, integration into clinical practice, algorithmic transparency
- MeSH terms
- Machine Learning, Big Data, Algorithms, Social Control, Formal, Reproducibility of Results
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