Big data and black-box medical algorithms

Price, W. Nicholson

doi:10.1126/scitranslmed.aao5333

Cited by 254Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare

Big data and black-box medical algorithms

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Price, W. Nicholson·2018·Science Translational Medicine

DOI 10.1126/scitranslmed.aao5333PMC PMC6345162

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Abstract

New machine-learning techniques entering medicine present challenges in validation, regulation, and integration into practice.

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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).
“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]
Perceived opacity of medical AI is an important barrier to its adoption.
“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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“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 ).”
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Black-Box Medicine
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Black-Box Medicine
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Authors: Price, W. N.
Affiliations: University of Copenhagen (DK)
Corresponding author: Price, W. Nicholson
ORCID: Price, W. Nicholson
Venue: Science Translational Medicine
Publisher: American Association for the Advancement of Science (AAAS)
Published: 2018-12-12
Volume: 10
Issue: 471
ISSN: 1946-6234 (ISSN-L)
1946-6242
DOI: 10.1126/scitranslmed.aao5333
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
Licence: www.sciencemag.org/about/science-licenses-journal-article-reuse (vor)
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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