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人工智能在医院和诊所中的作用:变革21世纪的医疗保健

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century.

作者信息

Maleki Varnosfaderani Shiva, Forouzanfar Mohamad

机构信息

Department of Electrical Engineering, Wayne State University, Detroit, MI 48202, USA.

Département de Génie des Systèmes, École de Technologie Supérieure (ÉTS), Université du Québec, Montréal, QC H3C 1K3, Canada.

出版信息

Bioengineering (Basel). 2024 Mar 29;11(4):337. doi: 10.3390/bioengineering11040337.


DOI:10.3390/bioengineering11040337
PMID:38671759
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11047988/
Abstract

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.

摘要

随着全球医疗保健系统面临成本不断攀升、可及性有限以及对个性化医疗需求不断增长等挑战,人工智能(AI)正成为推动变革的关键力量。这篇综述的动机源于迫切需要利用人工智能的潜力来缓解这些问题,旨在批判性地评估人工智能在不同医疗领域的整合情况。我们探讨人工智能如何赋能临床决策、优化医院运营与管理、完善医学图像分析,以及通过人工智能驱动的可穿戴设备如何彻底改变患者护理和监测方式。通过几个案例研究,我们回顾了人工智能如何改变特定的医疗领域,并讨论了 remaining challenges and possible solutions。此外,我们还将讨论评估人工智能医疗保健解决方案的方法、人工智能部署的伦理挑战,以及数据隐私和减轻偏差对于负责任地使用技术的重要性。通过对人工智能变革潜力的批判性评估,这篇综述使研究人员更深入地了解人工智能对医疗保健当前和未来的影响。它鼓励研究人员、临床医生和技术专家之间进行跨学科对话,以应对人工智能实施的复杂性,促进以道德标准、公平性和以患者为中心的方法为优先的人工智能驱动解决方案的发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c97e/11047988/0719cd2ccf61/bioengineering-11-00337-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c97e/11047988/f839bc51960d/bioengineering-11-00337-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c97e/11047988/0719cd2ccf61/bioengineering-11-00337-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c97e/11047988/f839bc51960d/bioengineering-11-00337-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c97e/11047988/0719cd2ccf61/bioengineering-11-00337-g002.jpg

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本文引用的文献

[1]
Informed consent for artificial intelligence in emergency medicine: A practical guide.

Am J Emerg Med. 2024-2

[2]
Addressing the challenges of AI-based telemedicine: Best practices and lessons learned.

J Educ Health Promot. 2023-9-29

[3]
Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care.

BMC Health Serv Res. 2023-10-26

[4]
Artificial intelligence in diabetes management: Advancements, opportunities, and challenges.

Cell Rep Med. 2023-10-17

[5]
Improved assessment of left ventricular ejection fraction using artificial intelligence in echocardiography: A comparative analysis with cardiac magnetic resonance imaging.

Int J Cardiol. 2024-1-1

[6]
Artificial Intelligence for Drug Discovery: Are We There Yet?

Annu Rev Pharmacol Toxicol. 2024-1-23

[7]
Implications for implementation and adoption of telehealth in developing countries: a systematic review of China's practices and experiences.

NPJ Digit Med. 2023-9-18

[8]
Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Modeling Studies: Development and Validation.

J Med Internet Res. 2023-8-31

[9]
Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study.

Lancet Oncol. 2023-8

[10]
Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review.

Breast Cancer Res. 2023-7-24

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