The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Should Health Care Demand Interpretable Artificial Intelligence or Accept “Black Box” Medicine?
Abstract
Health care applications of artificial intelligence (AI) have recently emerged. Artificial intelligence approaches, such as deep learning, rely on vast amounts of data and complex model structures ...
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In the citing paper
“thus, the development of such models requires the active involvement of clinicians (10).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [10]
In the citing paper
“Subsequently, this black-box phenomena (10) will be difficult for physicians to trust, and it will also be a challenge for the doctor-patient relationship since many physicians will find themselves unable to explain the diagnosis, prediction, or therapy (59).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [10]
In the citing paper
“Visualization techniques are often performed to construct qualitative clusters of patient cohorts to provide an intuitive interpretation of the learned representations [102].”
From Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review (2021) · cites this paper as [102]
Citations
Cites
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019) · Nature Medicine · doi:10.1038/s41591-018-0268-3 · Cited by 3,148
Scalable and accurate deep learning with electronic health records
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · cited 1×
“intelligence (AI), such as detection of atrial fibrillation from electrocardiography, retinopathy from optical coherence tomography, and in-hospital mortality risk from electronic health records, have emerged (1–3).”
BackgroundClinically applicable deep learning for diagnosis and referral in retinal disease
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0107-6 · Cited by 2,639
Scalable and accurate deep learning with electronic health records.
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · eScholarship (California Digital Library) · Cited by 2,242
van Walraven, C., Dhalla, I. A., Bell, C. M., Etchells, E. E., Stiell, I. G., Zarnke, K. B., Austin, P. C., & Forster, A. J. (2010) · Canadian Medical Association Journal · doi:10.1503/cmaj.091117 · Cited by 996
An interpretable machine learning model for accurate prediction of sepsis in the ICU
Nemati, S., Holder, A., Razmi, F., Stanley, M. D., Clifford, G. D., & Buchman, T. G. (2018) · Critical Care Medicine · doi:10.1097/ccm.0000000000002936 · Cited by 832
Cited by
Sorted by citations
The false hope of current approaches to explainable artificial intelligence in health care
Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00208-9 · Cited by 1,724
Explainable artificial intelligence: a comprehensive review
Minh, D., Wang, H. X., Li, Y. F., & Nguyen, T. N. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10088-y · Cited by 983
Harnessing multimodal data integration to advance precision oncology
Boehm, K. M., Khosravi, P., Vanguri, R., Gao, J., & Shah, S. P. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-021-00408-3 · Cited by 662
Aung, Y. Y. M., Wong, D. C. S., & Ting, D. S. W. (2021) · British Medical Bulletin · doi:10.1093/bmb/ldab016 · Cited by 511
Multimodal deep learning for Alzheimer's disease dementia assessment
Qiu, S., Miller, M. I., Joshi, P. S., Lee, J. C., Xue, C., Ni, Y., Wang, Y., De Anda-Duran, I., Hwang, P. H., Cramer, J. A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31037-5 · Cited by 420
Metadata
- Authors
- Wang, F., Kaushal, R., & Khullar, D.
- Affiliations
- Weill Cornell Medicine (US)
- NewYork–Presbyterian Hospital (US)
- Venue
- Annals of Internal Medicine
- Publisher
- American College of Physicians
- Published
- 2020-01-07
- Volume
- 172
- Issue
- 1
- Pages
- 59-60
- ISSN
- 0003-4819 (ISSN-L)
- 1539-3704
- OpenAlex
- W2995109209
- Cited by
- 244
- References
- 6
- Citations by year
- 2026: 19 · 2025: 31 · 2024: 38 · 2023: 59 · 2022: 32 · 2021: 35 · 2020: 30
- FWCI
- 11.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annals of Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- explainable AI, black-box models, health care demand, deep learning, model interpretability, algorithmic transparency, healthcare decision-making
- MeSH terms
- Artificial Intelligence, Delivery of Health Care, Humans, Models, Statistical
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