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患者对人工智能在医疗保健中的接受度、挑战及应用的认知:定性研究

Patients' Perceptions of Artificial Intelligence Acceptance, Challenges, and Use in Medical Care: Qualitative Study.

作者信息

Gundlack Jana, Thiel Carolin, Negash Sarah, Buch Charlotte, Apfelbacher Timo, Denny Kathleen, Christoph Jan, Mikolajczyk Rafael, Unverzagt Susanne, Frese Thomas

机构信息

Institute of General Practice and Family Medicine, Interdisciplinary Center of Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.

Institute for Medical Epidemiology, Biometrics and Informatics, Interdisciplinary Center for Health Sciences, Medical Faculty of the Martin Luther University Halle-Wittenberg, Halle (Saale), Germany.

出版信息

J Med Internet Res. 2025 May 15;27:e70487. doi: 10.2196/70487.


DOI:10.2196/70487
PMID:40373300
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12123243/
Abstract

BACKGROUND: Artificial intelligence (AI) is increasingly used in medical care, particularly in the areas of image recognition and processing. While its practical use in other areas is still limited, an understanding of patients' needs is essential for the practical and sustainable implementation of AI, which could further acceptance of new innovations. OBJECTIVE: The objective of this study was to explore patients' perceptions toward acceptance, challenges of implementation, and potential applications of AI in medical care. METHODS: The study used a qualitative research design. To capture a broad range of patient perspectives, we conducted semistructured focus groups (FGs). As a stimulus for the FGs and as an introduction to the topic, we presented a video defining AI and showing 3 potential AI applications in health care. Participants were recruited from different locations in the regions of Halle (Saale) and Erlangen, Germany; all but one group were from outpatient settings. We analyzed the data using a content analysis approach. RESULTS: A total of 35 patients (13 female and 22 male; age: range 23-92, median 50 years) participated in 6 focus groups. They highlighted that AI acceptance in medical care could be improved through user-friendly applications, clear instructions, feedback mechanisms, and a patient-centered approach. Perceived key barriers included data protection concerns, lack of human oversight, and profit-driven motives. Perceived challenges and requirements for AI implementation involved compatibility, training of end users, environmental sustainability, and adherence to quality standards. Potential AI application areas identified were diagnostics, image and data processing, and administrative tasks, though participants stressed that AI should remain a support tool, not an autonomous system. Psychology was an area where its use was opposed due to the need for human interaction. CONCLUSIONS: Patients were generally open to the use of AI in medical care as a support tool rather than as an independent decision-making system. Acceptance and successful use of AI in medical care could be achieved if it is easy to use, adapted to individual characteristics of the users, and accessible to everyone, with the primary aim of enhancing patient well-being. AI in health care requires a regulatory framework, quality standards, and monitoring to ensure socially fair and environmentally sustainable development. However, the successful implementation of AI in medical practice depends on overcoming the mentioned challenges and addressing user needs.

摘要

背景:人工智能(AI)在医疗保健领域的应用日益广泛,尤其是在图像识别和处理方面。虽然其在其他领域的实际应用仍然有限,但了解患者需求对于人工智能的实际和可持续应用至关重要,这有助于进一步推动新创新的接受度。 目的:本研究的目的是探讨患者对人工智能在医疗保健中的接受度、实施挑战和潜在应用的看法。 方法:本研究采用定性研究设计。为了获取广泛的患者观点,我们进行了半结构化焦点小组访谈(FGs)。作为焦点小组访谈的刺激因素和主题介绍,我们展示了一段定义人工智能并展示其在医疗保健中的3种潜在应用的视频。参与者来自德国哈雷(萨勒)和埃尔朗根地区的不同地点;除一组外,其他组均来自门诊机构。我们使用内容分析方法对数据进行了分析。 结果:共有35名患者(13名女性和22名男性;年龄范围:23 - 92岁,中位数50岁)参加了6个焦点小组访谈。他们强调,通过用户友好的应用程序、清晰的说明、反馈机制和以患者为中心的方法,可以提高医疗保健中人工智能的接受度。感知到的主要障碍包括对数据保护的担忧、缺乏人工监督和利润驱动的动机。人工智能实施的感知挑战和要求涉及兼容性、终端用户培训、环境可持续性以及遵守质量标准。确定的潜在人工智能应用领域包括诊断、图像和数据处理以及行政任务,不过参与者强调人工智能应始终是一种支持工具,而非自主系统。由于需要人际互动,心理学领域反对使用人工智能。 结论:患者普遍愿意在医疗保健中使用人工智能作为支持工具,而非独立的决策系统。如果人工智能易于使用、适应用户的个体特征且人人都能使用,以提高患者福祉为首要目标,那么在医疗保健中接受并成功使用人工智能是可以实现的。医疗保健中的人工智能需要一个监管框架、质量标准和监测,以确保社会公平和环境可持续发展。然而,人工智能在医疗实践中的成功实施取决于克服上述挑战并满足用户需求。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/923c/12123243/40d3e32c0a93/jmir_v27i1e70487_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/923c/12123243/f143853d2522/jmir_v27i1e70487_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/923c/12123243/40d3e32c0a93/jmir_v27i1e70487_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/923c/12123243/f143853d2522/jmir_v27i1e70487_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/923c/12123243/40d3e32c0a93/jmir_v27i1e70487_fig2.jpg

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本文引用的文献

[1]
FDA Perspective on the Regulation of Artificial Intelligence in Health Care and Biomedicine.

JAMA. 2025-1-21

[2]
Do patients prefer a human doctor, artificial intelligence, or a blend, and is this preference dependent on medical discipline? Empirical evidence and implications for medical practice.

Front Psychol. 2024-8-12

[3]
[The environmental impact of digital technology and artificial intelligence, in the era of digital pathology].

Ann Pathol. 2024-9

[4]
Operational greenhouse-gas emissions of deep learning in digital pathology: a modelling study.

Lancet Digit Health. 2024-1

[5]
Does artificial intelligence (AI) reduce ecological footprint? The role of globalization.

Environ Sci Pollut Res Int. 2023-12

[6]
Generative AI in Medical Imaging: Applications, Challenges, and Ethics.

J Med Syst. 2023-8-31

[7]
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Dtsch Arztebl Int. 2023-7-10

[8]
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BMC Med Inform Decis Mak. 2023-4-20

[9]
Current and potential applications of artificial intelligence in medical imaging practice: A narrative review.

J Med Imaging Radiat Sci. 2023-6

[10]
Surveying Public Perceptions of Artificial Intelligence in Health Care in the United States: Systematic Review.

J Med Internet Res. 2023-4-4

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