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AI's pivotal impact on redefining stakeholder roles and their interactions in medical education and health care.

作者信息

Reuben Jayne S, Meiri Hila, Arien-Zakay Hadar

机构信息

Texas A&M School of Dentistry, Dallas, TX, United States.

Department of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, United States.

出版信息

Front Digit Health. 2024 Nov 5;6:1458811. doi: 10.3389/fdgth.2024.1458811. eCollection 2024.


DOI:10.3389/fdgth.2024.1458811
PMID:39564581
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11573760/
Abstract

Artificial Intelligence (AI) has the potential to revolutionize medical training, diagnostics, treatment planning, and healthcare delivery while also bringing challenges such as data privacy, the risk of technological overreliance, and the preservation of critical thinking. This manuscript explores the impact of AI and Machine Learning (ML) on healthcare interactions, focusing on faculty, students, clinicians, and patients. AI and ML's early inclusion in the medical curriculum will support student-centered learning; however, all stakeholders will require specialized training to bridge the gap between medical practice and technological innovation. This underscores the importance of education in the ethical and responsible use of AI and emphasizing collaboration to maximize its benefits. This manuscript calls for a re-evaluation of interpersonal relationships within healthcare to improve the overall quality of care and safeguard the welfare of all stakeholders by leveraging AI's strengths and managing its risks.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/010e/11573760/07ec0e1e3088/fdgth-06-1458811-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/010e/11573760/07ec0e1e3088/fdgth-06-1458811-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/010e/11573760/07ec0e1e3088/fdgth-06-1458811-g001.jpg

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AI's pivotal impact on redefining stakeholder roles and their interactions in medical education and health care.

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引用本文的文献

[1]
Advancements in artificial intelligence transforming medical education: a comprehensive overview.

Med Educ Online. 2025-12

[2]
Exploring the social dimensions of AI integration in healthcare: a qualitative study of stakeholder views on challenges and opportunities.

BMJ Open. 2025-6-27

[3]
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[4]
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[5]
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本文引用的文献

[1]
Navigating the European Union Artificial Intelligence Act for Healthcare.

NPJ Digit Med. 2024-8-12

[2]
Ethical Considerations and Fundamental Principles of Large Language Models in Medical Education: Viewpoint.

J Med Internet Res. 2024-8-1

[3]
Critical thinking pedagogical practices in medical education: a systematic review.

Front Med (Lausanne). 2024-6-14

[4]
Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education.

Cureus. 2024-5-6

[5]
What Complexity Science Predicts About the Potential of Artificial Intelligence/Machine Learning to Improve Primary Care.

J Am Board Fam Med. 2024

[6]
Effectiveness of an Artificial Intelligence-Enabled Intervention for Detecting Clinical Deterioration.

JAMA Intern Med. 2024-5-1

[7]
Determining the recurrence rate of premature ventricular complexes and idiopathic ventricular tachycardia after radiofrequency catheter ablation with the help of designing a machine-learning model.

Regen Ther. 2024-3-10

[8]
The efficacy of artificial intelligence in urology: a detailed analysis of kidney stone-related queries.

World J Urol. 2024-3-14

[9]
Collaborative Enhancement of Consistency and Accuracy in US Diagnosis of Thyroid Nodules Using Large Language Models.

Radiology. 2024-3

[10]
Blended learning in nursing pharmacology: elevating cognitive skills, engagement and academic outcomes.

Front Pharmacol. 2024-2-22

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