加纳医学影像专业人员增强对人工智能系统信任的原则：一项全国性横断面研究。

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

作者信息

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

机构信息

Department of Medical Imaging, Faculty of Allied Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana; IMPACCT (Improving Palliative, Aged and Chronic Care Through Clinical Research and Translation), Faculty of Health, University of Technology Sydney, Australia.

Department of Medical Imaging, Faculty of Allied Health Sciences, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.

出版信息

Radiography (Lond). 2025 May;31(3):102953. doi: 10.1016/j.radi.2025.102953. Epub 2025 Apr 13.

DOI:10.1016/j.radi.2025.102953

PMID:40228323

Abstract

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.

摘要

引言

为了充分发挥人工智能（AI）系统在医学成像中的潜力，应对诸如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AI系统信任的原则。

方法

进行了一项匿名的、在线的、全国性的横断面调查。该调查包含与社会人口特征和AI可信原则相关的问题，包括“人类能动性与监督”、“技术稳健性与安全性”、“数据隐私、安全与治理”以及“透明度、公平性与问责制”。

结果

共有370名受访者完成了调查。在受访者中，66.5%（n = 246）是诊断放射技师。相当数量的受访者（n = 121，32.7%）表示对医学成像AI系统的工作方式了解很少或根本不了解。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AI系统的信任都很重要，综合平均得分为3.88±0.45。透明度、公平性与问责制的评分最高（4.27±0.58），而人类能动性与监督的平均得分为3.89±0.53。技术稳健性与安全性以及数据隐私、安全与治理的平均得分分别为3.79±0.61和3.58±0.65。