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Exploring knowledge, attitudes, and practices towards artificial intelligence among health professions' students in Jordan.探索约旦卫生专业学生对人工智能的知识、态度和实践。
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Factors governing the adoption of artificial intelligence in healthcare providers.影响医疗服务提供者采用人工智能的因素。
Discov Health Syst. 2022;1(1):4. doi: 10.1007/s44250-022-00004-8. Epub 2022 Oct 31.
3
The potential for artificial intelligence in healthcare.人工智能在医疗保健领域的潜力。
Future Healthc J. 2019 Jun;6(2):94-98. doi: 10.7861/futurehosp.6-2-94.
4
Dermatologist-level classification of skin cancer with deep neural networks.基于深度神经网络的皮肤癌皮肤科医生级分类。
Nature. 2017 Feb 2;542(7639):115-118. doi: 10.1038/nature21056. Epub 2017 Jan 25.
5
Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.预测未来——大数据、机器学习与临床医学。
N Engl J Med. 2016 Sep 29;375(13):1216-9. doi: 10.1056/NEJMp1606181.
6
Big data analytics in healthcare: promise and potential.医疗保健中的大数据分析:前景与潜力。
Health Inf Sci Syst. 2014 Feb 7;2:3. doi: 10.1186/2047-2501-2-3. eCollection 2014.

人工智能和大数据分析对医疗保健结果的影响:约旦医疗机构的实证研究。

Impact of AI and big data analytics on healthcare outcomes: An empirical study in Jordanian healthcare institutions.

作者信息

Al-Dmour Rand, Al-Dmour Hani, Basheer Amin Eatedal, Al-Dmour Ahmed

机构信息

The University of Jordan, Amman, Jordan.

Al-Ahliyya Amman University, Amman, Jordan.

出版信息

Digit Health. 2025 Jan 7;11:20552076241311051. doi: 10.1177/20552076241311051. eCollection 2025 Jan-Dec.

DOI:10.1177/20552076241311051
PMID:39777060
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11705344/
Abstract

Artificial intelligence (AI) and big data analytics are transforming healthcare globally and in Jordan. This study investigates the effects of AI and big data analytics on healthcare outcomes in Jordanian healthcare institutions. A comprehensive model is proposed to understand the antecedents of healthcare outcomes, including the impact of perceived ease of use, perceived usefulness, and organizational capabilities. Data were collected from 400 structured questionnaires, with a final sample size of 288 respondents, and analyzed using partial least squares structural equation modeling. The findings reveal that AI technologies significantly improve diagnostic accuracy and treatment planning, while big data analytics enhances operational efficiency and patient care. However, the comparative influence of AI on different healthcare processes was less significant. Additionally, robust organizational capabilities effectively enhanced the adoption and impact of AI and big data analytics. The study highlights the mediating roles of perceived ease of use and usefulness in the relationship between technology adoption and healthcare outcomes. Understanding the interplay between AI, big data analytics, and healthcare delivery is crucial for policymakers, healthcare administrators, and technology developers to develop effective strategies that improve patient care and operational efficiency. This study recommends investing in user-friendly AI and big data analytics tools, enhancing organizational capabilities, and providing comprehensive training for healthcare professionals. Future research should extend this study to different cultural contexts to validate the findings and contribute further to the literature on AI and healthcare.

摘要

人工智能(AI)和大数据分析正在改变全球及约旦的医疗保健行业。本研究调查了人工智能和大数据分析对约旦医疗机构医疗保健成果的影响。提出了一个综合模型来理解医疗保健成果的影响因素,包括感知易用性、感知有用性和组织能力的影响。通过400份结构化问卷收集数据,最终样本量为288名受访者,并使用偏最小二乘结构方程模型进行分析。研究结果表明,人工智能技术显著提高了诊断准确性和治疗规划,而大数据分析提高了运营效率和患者护理水平。然而,人工智能对不同医疗保健流程的比较影响较小。此外,强大的组织能力有效地增强了人工智能和大数据分析的采用及影响。该研究强调了感知易用性和有用性在技术采用与医疗保健成果之间关系中的中介作用。理解人工智能、大数据分析与医疗保健服务之间的相互作用,对于政策制定者、医疗保健管理人员和技术开发者制定改善患者护理和运营效率的有效策略至关重要。本研究建议投资于用户友好的人工智能和大数据分析工具,增强组织能力,并为医疗保健专业人员提供全面培训。未来的研究应将本研究扩展到不同的文化背景,以验证研究结果,并为人工智能与医疗保健领域的文献做出进一步贡献。