提高加纳医学影像专业人员对人工智能系统信任度的原则：全国横断面研究

IF 2.5 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Radiography Pub Date : 2025-04-13 DOI:10.1016/j.radi.2025.102953

A. Donkor , D. Kumi , E. Amponsah , V. Della Atuwo-Ampoh

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引用次数: 0

摘要

为了充分发挥人工智能（AI）系统在医学成像中的潜力，解决网络恐怖主义等挑战以促进信任和接受至关重要。本研究旨在从加纳医学成像专业人员的角度确定增强对人工智能系统信任的原则。方法采用匿名、在线、全国横断面调查方法。该调查包含与社会人口特征和人工智能可信赖原则相关的问题，包括“人类机构和监督”、“技术稳健性和安全性”、“数据隐私、安全和治理”以及“透明度、公平性和问责制”。结果共有370名受访者完成调查。在受访者中，66.5% （n = 246）为放射诊断技师。相当多的受访者（n = 121, 32.7%）表示，他们对医学成像人工智能系统的工作原理知之甚少或根本不了解。总体而言，54.9% （n = 203）的受访者同意或强烈同意这四项原则中的每一项对于增强对医学成像人工智能系统的信任都很重要，综合平均得分为3.88±0.45。透明度、公平性和问责性得分最高（4.27±0.58），而人力代理和监督的平均得分为3.89±0.53。技术稳健性和安全性以及数据隐私性、安全性和治理的平均得分分别为3.79±0.61和3.58±0.65。结论加纳医学成像专业人员一致认为，人的能动性、技术稳健性、数据隐私和透明度是增强对人工智能系统信任的重要原则；然而，未来的计划需要包括医疗成像人工智能教育干预措施，以提高加纳医疗成像专业人员的人工智能素养。对实践的启示提出的证据应该鼓励组织设计和部署值得信赖的医疗成像人工智能系统。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Principles for enhancing trust in artificial intelligence systems among medical imaging professionals in Ghana: A nationwide cross-sectional study

Introduction

To realise the full potential of artificial intelligence (AI) systems in medical imaging, it is crucial to address challenges, such as cyberterrorism to foster trust and acceptance. This study aimed to determine the principles that enhance trust in AI systems from the perspective of medical imaging professionals in Ghana.

Methods

An anonymous, online, nationwide cross-sectional survey was conducted. The survey contained questions related to socio-demographic characteristics and AI trustworthy principles, including “human agency and oversight”, “technical robustness and safety”, “data privacy, security and governance” and “transparency, fairness and accountability”.

Results

A total of 370 respondents completed the survey. Among the respondents, 66.5 % (n = 246) were diagnostic radiographers. Considerable number of respondents (n = 121, 32.7 %) reported having little or no understanding of how medical imaging AI systems work. Overall, 54.9 % (n = 203) of the respondents agreed or strongly agreed that each of the four principles was important to enhance trust in medical imaging AI systems, with a composite mean score of 3.88 ± 0.45. Transparency, fairness and accountability had the highest rating (4.27 ± 0.58), whereas the mean score for human agency and oversight was 3.89 ± 0.53. Technical robustness and safety as well as data privacy, security and governance obtained mean scores of 3.79 ± 0.61 and 3.58 ± 0.65, respectively.

Conclusion

Medical imaging professionals in Ghana agreed that human agency, technical robustness, data privacy and transparency are important principles to enhance trust in AI systems; however, future plans including medical imaging AI educational interventions are required to improve AI literacy among medical imaging professionals in Ghana.

Implications for practice

The evidence presented should encourage organisations to design and deploy trustworthy medical imaging AI systems.

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来源期刊

Radiography RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

4.70

自引率

34.60%

发文量

169

审稿时长

63 days

期刊介绍： Radiography is an International, English language, peer-reviewed journal of diagnostic imaging and radiation therapy. Radiography is the official professional journal of the College of Radiographers and is published quarterly. Radiography aims to publish the highest quality material, both clinical and scientific, on all aspects of diagnostic imaging and radiation therapy and oncology.