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On the practical, ethical, and legal necessity of clinical Artificial Intelligence explainability: an examination of key arguments.

作者信息

Blackman Justin, Veerapen Richard

机构信息

Island Medical Program, Faculty of Medicine, University of British Columbia, University of Victoria, Victoria, BC, Canada.

School of Health Information Science, University of Victoria, Victoria, BC, Canada.

出版信息

BMC Med Inform Decis Mak. 2025 Mar 5;25(1):111. doi: 10.1186/s12911-025-02891-2.


DOI:10.1186/s12911-025-02891-2
PMID:40045339
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11881432/
Abstract

The necessity for explainability of artificial intelligence technologies in medical applications has been widely discussed and heavily debated within the literature. This paper comprises a systematized review of the arguments supporting and opposing this purported necessity. Both sides of the debate within the literature are quoted to synthesize discourse on common recurring themes and subsequently critically analyze and respond to it. While the use of autonomous black box algorithms is compellingly discouraged, the same cannot be said for the whole of medical artificial intelligence technologies that lack explainability. We contribute novel comparisons of unexplainable clinical artificial intelligence tools, diagnosis of idiopathy, and diagnoses by exclusion, to analyze implications on patient autonomy and informed consent. Applying a novel approach using comparisons with clinical practice guidelines, we contest the claim that lack of explainability compromises clinician due diligence and undermines epistemological responsibility. We find it problematic that many arguments in favour of the practical, ethical, or legal necessity of clinical artificial intelligence explainability conflate the use of unexplainable AI with automated decision making, or equate the use of clinical artificial intelligence with the exclusive use of clinical artificial intelligence.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb0e/11881432/780bd5d8386e/12911_2025_2891_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb0e/11881432/acae6b6481bc/12911_2025_2891_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb0e/11881432/3c69cbdb1d93/12911_2025_2891_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb0e/11881432/0bfa360d278b/12911_2025_2891_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb0e/11881432/780bd5d8386e/12911_2025_2891_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb0e/11881432/acae6b6481bc/12911_2025_2891_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb0e/11881432/3c69cbdb1d93/12911_2025_2891_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb0e/11881432/0bfa360d278b/12911_2025_2891_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cb0e/11881432/780bd5d8386e/12911_2025_2891_Fig4_HTML.jpg

相似文献

[1]
On the practical, ethical, and legal necessity of clinical Artificial Intelligence explainability: an examination of key arguments.

BMC Med Inform Decis Mak. 2025-3-5

[2]
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective.

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[3]
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[4]
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[5]
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[6]
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[7]
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[8]
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[9]
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[10]
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引用本文的文献

[1]
Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions.

MedComm (2020). 2025-6-25

[2]
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本文引用的文献

[1]
The ethical requirement of explainability for AI-DSS in healthcare: a systematic review of reasons.

BMC Med Ethics. 2024-10-1

[2]
Heterogeneity and predictors of the effects of AI assistance on radiologists.

Nat Med. 2024-3

[3]
Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance.

J Nucl Med. 2023-10

[4]
Editorial: Transparent machine learning in bio-medicine.

Front Bioinform. 2023-8-4

[5]
Bioethics Without Theory?

Camb Q Healthc Ethics. 2024-4

[6]
On the Justified Use of AI Decision Support in Evidence-Based Medicine: Validity, Explainability, and Responsibility.

Camb Q Healthc Ethics. 2023-6-9

[7]
Black-box assisted medical decisions: AI power vs. ethical physician care.

Med Health Care Philos. 2023-9

[8]
Artificial Intelligence Algorithms Need to Be Explainable-or Do They?

J Nucl Med. 2023-6

[9]
Racial bias in cesarean decision-making.

Am J Obstet Gynecol MFM. 2023-5

[10]
"Just" accuracy? Procedural fairness demands explainability in AI-based medical resource allocations.

AI Soc. 2022-12-21

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