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Benefits and Risks of AI in Health Care: Narrative Review.

作者信息

Chustecki Margaret

机构信息

Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States.

出版信息

Interact J Med Res. 2024 Nov 18;13:e53616. doi: 10.2196/53616.


DOI:10.2196/53616
PMID:39556817
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11612599/
Abstract

BACKGROUND: The integration of artificial intelligence (AI) into health care has the potential to transform the industry, but it also raises ethical, regulatory, and safety concerns. This review paper provides an in-depth examination of the benefits and risks associated with AI in health care, with a focus on issues like biases, transparency, data privacy, and safety. OBJECTIVE: This study aims to evaluate the advantages and drawbacks of incorporating AI in health care. This assessment centers on the potential biases in AI algorithms, transparency challenges, data privacy issues, and safety risks in health care settings. METHODS: Studies included in this review were selected based on their relevance to AI applications in health care, focusing on ethical, regulatory, and safety considerations. Inclusion criteria encompassed peer-reviewed articles, reviews, and relevant research papers published in English. Exclusion criteria included non-peer-reviewed articles, editorials, and studies not directly related to AI in health care. A comprehensive literature search was conducted across 8 databases: OVID MEDLINE, OVID Embase, OVID PsycINFO, EBSCO CINAHL Plus with Full Text, ProQuest Sociological Abstracts, ProQuest Philosopher's Index, ProQuest Advanced Technologies & Aerospace, and Wiley Cochrane Library. The search was last updated on June 23, 2023. Results were synthesized using qualitative methods to identify key themes and findings related to the benefits and risks of AI in health care. RESULTS: The literature search yielded 8796 articles. After removing duplicates and applying the inclusion and exclusion criteria, 44 studies were included in the qualitative synthesis. This review highlights the significant promise that AI holds in health care, such as enhancing health care delivery by providing more accurate diagnoses, personalized treatment plans, and efficient resource allocation. However, persistent concerns remain, including biases ingrained in AI algorithms, a lack of transparency in decision-making, potential compromises of patient data privacy, and safety risks associated with AI implementation in clinical settings. CONCLUSIONS: In conclusion, while AI presents the opportunity for a health care revolution, it is imperative to address the ethical, regulatory, and safety challenges linked to its integration. Proactive measures are required to ensure that AI technologies are developed and deployed responsibly, striking a balance between innovation and the safeguarding of patient well-being.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b26/11612599/c78081a6585d/ijmr_v13i1e53616_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b26/11612599/c78081a6585d/ijmr_v13i1e53616_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b26/11612599/c78081a6585d/ijmr_v13i1e53616_fig1.jpg

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本文引用的文献

[1]
Improving large language models for clinical named entity recognition via prompt engineering.

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[2]
Chatbots and Large Language Models in Radiology: A Practical Primer for Clinical and Research Applications.

Radiology. 2024-1

[3]
Choosing human over AI doctors? How comparative trust associations and knowledge relate to risk and benefit perceptions of AI in healthcare.

Risk Anal. 2024-4

[4]
Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study.

JMIR Med Inform. 2023-2-24

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Ethics and governance of trustworthy medical artificial intelligence.

BMC Med Inform Decis Mak. 2023-1-13

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A large language model for electronic health records.

NPJ Digit Med. 2022-12-26

[7]
Using a classification model for determining the value of liver radiological reports of patients with colorectal cancer.

Front Oncol. 2022-11-21

[8]
Inclusion of Clinicians in the Development and Evaluation of Clinical Artificial Intelligence Tools: A Systematic Literature Review.

Front Psychol. 2022-4-7

[9]
Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health.

Annu Rev Public Health. 2022-4-5

[10]
The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare.

Br Med Bull. 2021-9-10

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