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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Development of deep learning models requires the active involvement of clinicians.
“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]
The black-box phenomena will be difficult for physicians to trust and will be a challenge for the doctor-patient relationship because many physicians will find themselves unable to explain the diagnosis, prediction, or therapy.
“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]
Visualization techniques are often performed to construct qualitative clusters of patient cohorts to provide an intuitive interpretation of the learned representations.
“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]
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Self-citationGashi, M., Vuković, M., Jekic, N., Thalmann, S., Holzinger, A., Jean-Quartier, C., & Jeanquartier, F. (2022) · BioMedInformatics · doi:10.3390/biomedinformatics2010009 · Cited by 25 · see citing works
AI hybrid survival assessment for advanced heart failure patients with renal dysfunction
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Explainable Deep Attention Active Learning for Sentimental Analytics of Mental Disorder
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The Role of Artificial Intelligence and Machine Learning in Clinical Cardiac Electrophysiology
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Saliency of breast lesions in breast cancer detection using artificial intelligence
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Artificial Intelligence in Cardiology: An Australian Perspective
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National Use of Artificial Intelligence for Eye Screening in Singapore
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Deep learning detects and visualizes bleeding events in electronic health records
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- 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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