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The potential for artificial intelligence to transform healthcare: perspectives from international health leaders.

作者信息

Silcox Christina, Zimlichmann Eyal, Huber Katie, Rowen Neil, Saunders Robert, McClellan Mark, Kahn Charles N, Salzberg Claudia A, Bates David W

机构信息

Duke-Margolis Institute for Health Policy, Duke University, Washington, DC, USA &, Durham, NC, USA.

Sheba Medical Center, Ramat Gan, Israel.

出版信息

NPJ Digit Med. 2024 Apr 9;7(1):88. doi: 10.1038/s41746-024-01097-6.


DOI:10.1038/s41746-024-01097-6
PMID:38594477
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11004157/
Abstract

Artificial intelligence (AI) has the potential to transform care delivery by improving health outcomes, patient safety, and the affordability and accessibility of high-quality care. AI will be critical to building an infrastructure capable of caring for an increasingly aging population, utilizing an ever-increasing knowledge of disease and options for precision treatments, and combatting workforce shortages and burnout of medical professionals. However, we are not currently on track to create this future. This is in part because the health data needed to train, test, use, and surveil these tools are generally neither standardized nor accessible. There is also universal concern about the ability to monitor health AI tools for changes in performance as they are implemented in new places, used with diverse populations, and over time as health data may change. The Future of Health (FOH), an international community of senior health care leaders, collaborated with the Duke-Margolis Institute for Health Policy to conduct a literature review, expert convening, and consensus-building exercise around this topic. This commentary summarizes the four priority action areas and recommendations for health care organizations and policymakers across the globe that FOH members identified as important for fully realizing AI's potential in health care: improving data quality to power AI, building infrastructure to encourage efficient and trustworthy development and evaluations, sharing data for better AI, and providing incentives to accelerate the progress and impact of AI.

摘要

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本文引用的文献

[1]
A Nationwide Network of Health AI Assurance Laboratories.

JAMA. 2024-1-16

[2]
Identifying facilitators of and barriers to the adoption of dynamic consent in digital health ecosystems: a scoping review.

BMC Med Ethics. 2023-12-1

[3]
Exploring the barriers and facilitators to implementing electronic health records in a middle-income country: a qualitative study from South Africa.

Front Digit Health. 2023-8-4

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NAM Perspect. 2022-6-27

[5]
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Front Digit Health. 2021-3-30

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External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients.

JAMA Intern Med. 2021-8-1

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