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助力医生对医学影像诊断中的人工智能应用做出明智的采用决策:定性研究

Enabling Physicians to Make an Informed Adoption Decision on Artificial Intelligence Applications in Medical Imaging Diagnostics: Qualitative Study.

作者信息

Hennrich Jasmin, Doctor Eileen, Körner Marc-Fabian, Lederman Reeva, Eymann Torsten

机构信息

University of Bayreuth, Bayreuth, Germany.

FIM Research Center, Bayreuth, Germany.

出版信息

J Med Internet Res. 2025 Aug 12;27:e63668. doi: 10.2196/63668.


DOI:10.2196/63668
PMID:40795316
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12342689/
Abstract

BACKGROUND: Artificial intelligence (AI) applications hold great promise for improving accuracy and efficiency in medical imaging diagnostics. However, despite the expected benefit of AI applications, widespread adoption of the technology is progressing slower than expected due to technological, organizational, and regulatory obstacles, and user-related barriers, with physicians playing a central role in adopting AI applications. OBJECTIVE: This study aims to provide guidance on enabling physicians to make an informed adoption decision regarding AI applications by identifying and discussing measures to address key barriers from physicians' perspectives. METHODS: We used a 2-step qualitative research approach. First, we conducted a structured literature review by screening 865 papers to identify potential enabling measures. Second, we interviewed 14 experts to evaluate the literature-based measures and enriched them. RESULTS: By analyzing the literature and interview transcripts, we revealed 11 measures, categorized into Enabling Adoption Decision Measures (eg, educating physicians, preparing future physicians, and providing transparency) and Supporting Adoption Measures (eg, implementation guidelines and AI marketplaces). These measures aim to inform physicians' decisions and support the adoption process. CONCLUSIONS: This study provides a comprehensive overview of measures to enable physicians to make an informed adoption decision on AI applications in medical imaging diagnostics. Thereby, we are the first to give specific recommendations on how to realize the potential of AI applications in medical imaging diagnostics from a user perspective.

摘要

背景:人工智能(AI)应用在提高医学影像诊断的准确性和效率方面具有巨大潜力。然而,尽管人工智能应用有望带来益处,但由于技术、组织、监管障碍以及与用户相关的障碍,该技术的广泛采用进展比预期要慢,而医生在采用人工智能应用方面起着核心作用。 目的:本研究旨在通过从医生的角度识别和讨论应对关键障碍的措施,为使医生能够就是否采用人工智能应用做出明智决策提供指导。 方法:我们采用了两步定性研究方法。首先,我们通过筛选865篇论文进行结构化文献综述,以确定潜在的促进措施。其次,我们采访了14位专家,以评估基于文献的措施并对其进行补充。 结果:通过分析文献和访谈记录,我们揭示了11项措施,分为促进采用决策措施(例如,教育医生、培养未来医生以及提供透明度)和支持采用措施(例如,实施指南和人工智能市场)。这些措施旨在为医生的决策提供信息并支持采用过程。 结论:本研究全面概述了使医生能够就是否在医学影像诊断中采用人工智能应用做出明智决策的措施。因此,我们是第一个从用户角度就如何实现人工智能应用在医学影像诊断中的潜力给出具体建议的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebf0/12342689/880fe61ffd28/jmir-v27-e63668-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebf0/12342689/880fe61ffd28/jmir-v27-e63668-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ebf0/12342689/880fe61ffd28/jmir-v27-e63668-g001.jpg

相似文献

[1]
Enabling Physicians to Make an Informed Adoption Decision on Artificial Intelligence Applications in Medical Imaging Diagnostics: Qualitative Study.

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[6]
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[7]
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本文引用的文献

[1]
Capturing artificial intelligence applications' value proposition in healthcare - a qualitative research study.

BMC Health Serv Res. 2024-4-3

[2]
A multinational study on artificial intelligence adoption: Clinical implementers' perspectives.

Int J Med Inform. 2024-4

[3]
The underuse of AI in the health sector: Opportunity costs, success stories, risks and recommendations.

Health Technol (Berl). 2024

[4]
Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review.

Artif Intell Med. 2024-1

[5]
Building Diversity, Equity, and Inclusion Within Radiology Artificial Intelligence: Representation Matters, From Data to the Workforce.

J Am Coll Radiol. 2023-9

[6]
Artificial intelligence in ophthalmology: The path to the real-world clinic.

Cell Rep Med. 2023-7-18

[7]
Bias in artificial intelligence algorithms and recommendations for mitigation.

PLOS Digit Health. 2023-6-22

[8]
Digital Education for the Deployment of Artificial Intelligence in Health Care.

J Med Internet Res. 2023-6-22

[9]
Awareness of Artificial Intelligence in Medical Imaging Among Radiologists and Radiologic Technologists.

Cureus. 2023-4-30

[10]
Increasing acceptance of medical AI: The role of medical staff participation in AI development.

Int J Med Inform. 2023-7

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