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IF 1.8 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of Medical Radiation Sciences Pub Date : 2024-12-20 DOI:10.1002/jmrs.843

Milena Lewandowska, Deborah Street, Jackie Yim, Scott Jones, Rosalie Viney

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引用次数: 0

摘要

导言：人工智能（AI）在放射治疗中的应用有望应对各种挑战，如医护人员短缺、提高效率和治疗计划差异等。更多地采用人工智能有可能使治疗方案标准化、提高质量、改善患者疗效并降低成本。然而，人工智能也有缺点，如对就业的影响和算法偏差，因此如何权衡利弊至关重要。我们开展了一项离散选择实验（DCE），以研究放射肿瘤专业人员认为在放射治疗计划中采用人工智能最重要的相关特征：放射肿瘤学专业人员完成了一项在线离散选择实验，以表达他们对用于放射治疗计划的人工智能系统的偏好，这些偏好由五个属性描述，每个属性有 2-4 个等级：准确性、自动化、探索能力、与其他系统的兼容性以及对工作量的影响。调查还包括对人工智能的态度问题。调查结果：82 位受访者完成了调查。结果表明，他们更喜欢能最大程度节省时间、提供人工智能推理解释（包括深入解释和基本解释）的人工智能系统。与人工系统相比，他们还更青睐能提高轮廓精度的系统。受访者强调了人工智能系统具有成本效益的重要性，同时也认识到了人工智能对专业角色、职责和服务提供的影响：本研究提供了有关放射肿瘤专业人员在治疗计划中对人工智能的优先考虑的重要信息。这项研究的结果可为今后有关放疗中人工智能驱动技术的经济评估和管理视角的研究提供参考。

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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Artificial intelligence in radiation therapy treatment planning: A discrete choice experiment.

Introduction: The application of artificial intelligence (AI) in radiation therapy holds promise for addressing challenges, such as healthcare staff shortages, increased efficiency and treatment planning variations. Increased AI adoption has the potential to standardise treatment protocols, enhance quality, improve patient outcomes, and reduce costs. However, drawbacks include impacts on employment and algorithmic biases, making it crucial to navigate trade-offs. A discrete choice experiment (DCE) was undertaken to examine the AI-related characteristics radiation oncology professionals think are most important for adoption in radiation therapy treatment planning.

Methods: Radiation oncology professionals completed an online discrete choice experiment to express their preferences about AI systems for radiation therapy planning which were described by five attributes, each with 2-4 levels: accuracy, automation, exploratory ability, compatibility with other systems and impact on workload. The survey also included questions about attitudes to AI. Choices were modelled using mixed logit regression.

Results: The survey was completed by 82 respondents. The results showed they preferred AI systems that offer the largest time saving, and that provide explanations of the AI reasoning (both in-depth and basic). They also favoured systems that provide improved contouring precision compared with manual systems. Respondents emphasised the importance of AI systems being cost-effective, while also recognising AI's impact on professional roles, responsibilities, and service delivery.

Conclusions: This study provides important information about radiation oncology professionals' priorities for AI in treatment planning. The findings from this study can be used to inform future research on economic evaluations and management perspectives of AI-driven technologies in radiation therapy.

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来源期刊

Journal of Medical Radiation Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-

CiteScore

3.20

自引率

4.80%

发文量

审稿时长

8 weeks

期刊介绍： Journal of Medical Radiation Sciences (JMRS) is an international and multidisciplinary peer-reviewed journal that accepts manuscripts related to medical imaging / diagnostic radiography, radiation therapy, nuclear medicine, medical ultrasound / sonography, and the complementary disciplines of medical physics, radiology, radiation oncology, nursing, psychology and sociology. Manuscripts may take the form of: original articles, review articles, commentary articles, technical evaluations, case series and case studies. JMRS promotes excellence in international medical radiation science by the publication of contemporary and advanced research that encourages the adoption of the best clinical, scientific and educational practices in international communities. JMRS is the official professional journal of the Australian Society of Medical Imaging and Radiation Therapy (ASMIRT) and the New Zealand Institute of Medical Radiation Technology (NZIMRT).