Cited by 244Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

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Should Health Care Demand Interpretable Artificial Intelligence or Accept “Black Box” Medicine?

Wang, Fei, Kaushal, Rainu, Khullar, Dhruv·2020·Annals of Internal Medicine

DOI 10.7326/m19-2548

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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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Authors: Wang, F., Kaushal, R., & Khullar, D.
Affiliations: Weill Cornell Medicine (US)
NewYork–Presbyterian Hospital (US)
ORCID: Wang, Fei
Kaushal, Rainu
Khullar, Dhruv
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
DOI: 10.7326/m19-2548
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
Licence: www.acpjournals.org/journal/aim/text-and-data-mining (tdm)
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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Should Health Care Demand Interpretable Artificial Intelligence or Accept “Black Box” Medicine?

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