FUTURE-AI：医学影像中可信人工智能的指导原则与共识建议
FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging
AI总结：
本文提出FUTURE-AI框架，将可信AI指导原则转化为医学影像具体实施指南，涵盖公平性等六要素，并提供成熟度检查清单，以促进临床部署。
摘要：
人工智能（AI）的最新进展，结合当今临床系统产生的大量数据，推动了成像AI解决方案在医学影像全价值链中的发展，包括图像重建、医学图像分割、基于图像的诊断和治疗规划。尽管AI在医学影像中取得了成功并具有未来潜力，许多利益相关者仍担忧成像AI解决方案的潜在风险和伦理影响，这些解决方案被认为复杂、不透明，且在关键临床应用中难以理解、使用和信任。为解决这些担忧和风险，FUTURE-AI框架被提出，该框架源自全球多领域专家共识，包含提高医疗保健中AI的信任度、安全性和采用率的指导原则。在本文中，我们将通用的FUTURE-AI医疗保健原则转化为针对医学影像社区需求定制的简洁且具体的AI实施指南。为此，我们仔细评估了FUTURE-AI框架的每个组成部分，包括（i）公平性，（ii）普遍性，（iii）可追溯性，（iv）可用性，（v）稳健性和（vi）可解释性，并基于五个欧洲大型AI健康影像项目的累积AI实施经验，分别定义了具体的最佳实践。我们为具体的逐步医学影像开发指南附上了一个实用的AI解决方案成熟度检查清单，从而使AI开发团队能够设计、评估、维护和部署在技术、临床和伦理上可信的成像AI解决方案到临床实践中。
英文摘要：
The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of medical imaging, including image reconstruction, medical image segmentation, image-based diagnosis and treatment planning. Notwithstanding the successes and future potential of AI in medical imaging, many stakeholders are concerned of the potential risks and ethical implications of imaging AI solutions, which are perceived as complex, opaque, and difficult to comprehend, utilise, and trust in critical clinical applications. Addressing these concerns and risks, the FUTURE-AI framework has been proposed, which, sourced from a global multi-domain expert consensus, comprises guiding principles for increased trust, safety, and adoption for AI in healthcare. In this paper, we transform the general FUTURE-AI healthcare principles to a concise and specific AI implementation guide tailored to the needs of the medical imaging community. To this end, we carefully assess each building block of the FUTURE-AI framework consisting of (i) Fairness, (ii) Universality, (iii) Traceability, (iv) Usability, (v) Robustness and (vi) Explainability, and respectively define concrete best practices based on accumulated AI implementation experiences from five large European projects on AI in Health Imaging. We accompany our concrete step-by-step medical imaging development guide with a practical AI solution maturity checklist, thus enabling AI development teams to design, evaluate, maintain, and deploy technically, clinically and ethically trustworthy imaging AI solutions into clinical practice.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
