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The underuse of AI in the health sector: Opportunity costs, success stories, risks and recommendations.

作者信息

Pagallo Ugo, O'Sullivan Shane, Nevejans Nathalie, Holzinger Andreas, Friebe Michael, Jeanquartier Fleur, Jean-Quartier Claire, Miernik Arkadiusz

机构信息

Law School, University of Turin, Turin, Italy.

Department of Urology, Faculty of Medicine, University of Freiburg - Medical Centre, Freiburg im Breisgau, Germany.

出版信息

Health Technol (Berl). 2024;14(1):1-14. doi: 10.1007/s12553-023-00806-7. Epub 2023 Dec 12.


DOI:10.1007/s12553-023-00806-7
PMID:38229886
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10788319/
Abstract

PURPOSE: This contribution explores the underuse of artificial intelligence (AI) in the health sector, what this means for practice, and how much the underuse can cost. Attention is drawn to the relevance of an issue that the European Parliament has outlined as a "major threat" in 2020. At its heart is the risk that research and development on trusted AI systems for medicine and digital health will pile up in lab centers without generating further practical relevance. Our analysis highlights why researchers, practitioners and especially policymakers, should pay attention to this phenomenon. METHODS: The paper examines the ways in which governments and public agencies are addressing the underuse of AI. As governments and international organizations often acknowledge the limitations of their own initiatives, the contribution explores the causes of the current issues and suggests ways to improve initiatives for digital health. RESULTS: Recommendations address the development of standards, models of regulatory governance, assessment of the opportunity costs of underuse of technology, and the urgency of the problem. CONCLUSIONS: The exponential pace of AI advances and innovations makes the risks of underuse of AI increasingly threatening.

摘要

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[1]
Large language models in medicine.

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[2]
Using ChatGPT to evaluate cancer myths and misconceptions: artificial intelligence and cancer information.

JNCI Cancer Spectr. 2023-3-1

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[4]
Federated learning enables big data for rare cancer boundary detection.

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[5]
Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis.

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Human-machine teaming is key to AI adoption: clinicians' experiences with a deployed machine learning system.

NPJ Digit Med. 2022-7-21

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JACC Cardiovasc Imaging. 2022-2

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Nat Biomed Eng. 2021-6

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Mutation-based clustering and classification analysis reveals distinctive age groups and age-related biomarkers for glioma.

BMC Med Inform Decis Mak. 2021-2-27

[10]
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