Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer's Disease (Parent grant)

阿尔茨海默病深度学习神经影像内表型遗传学补充 疾病。 摘要 AI/ML为生物医学研究人员提供了前所未有的机会，例如快速识别 疾病的遗传基础，包括阿尔茨海默病(AD)。例如，家长拨款提出了新的 基于深度学习的方法，用于从神经成像数据中获得与AD相关的内表型，以及 将这些内表型与遗传数据相关联。它希望发现与AD相关的新基因，这可能 从而更好地了解阿尔茨海默病的分子基础和潜在的新疗法。 然而，AI/ML方法在数据采集的设计和实现中可能会带来潜在的偏差， 训练数据，以及算法开发。这种偏见可能会导致有问题的研究结果，并可能进一步 造成健康差距的因素。近几年来，学术界和社会上对 伦理人工智能的原则；然而，有有限的经验数据或基于证据的机制具有 展示了研究人员在伦理问题上的知识、态度或观点 开发AI/ML算法或他们如何考虑将研究伦理融入他们的工作中。此外， 如何开展和提供有效的人工智能伦理教育是另一个需要系统科学的问题 问询。 这一提议的补充将人工智能研究人员和生物伦理学家聚集在一起，创建了第一个衡量标准 测量医学人工智能研究人员对人工智能研究原则(慈善、非恶意、 正义和责任)以及他们对如何使用这些原则来指导道德决策的知识 通过使用案例研究小插曲，利用人工智能进行阿尔茨海默病研究。 为了创造有效的面向AI AD研究人员的AI伦理教育，我们引入了虚拟现实认真 游戏设计师将开发一款基于VR的互动应用程序，用于道德决策教育 医学人工智能在研究中。这种互动和身临其境的教学材料交付模式已经 与传统教育相比，显示出更多的参与度、幸福感和更高的有效性 频道。从研究人员和社区咨询委员会收集的信息也将告知 开发这一人工智能伦理培训计划。VR应用的可用性和有效性将是 使用测试后调查和焦点小组进行评估。

Supplement to Genetics of deep-learning-derived neuroimaging endophenotypes for Alzheimer’s Disease. Abstract AI/ML provides unprecedented opportunities for biomedical researchers, such as the quick identification of the genetic basis of diseases, including Alzheimer’s Disease (AD). For instance, the parent grant proposes new deep learning based approaches for deriving AD-relevant endophenotypes from neuroimaging data, and associating these endophenotypes to genetic data. It expects to discover new genes relevant to AD which may lead to a better understanding of the molecular basis of AD and potential new treatments. However, AI/ML methods could bring potential biases in the design and implementation of data collection, training data, as well as algorithm development. Such biases may lead to problematic findings and may further contribute to health disparity. Recent years have witnessed the heightened scholarly and societal discussion of principles of ethical AI; however, there is limited empirical data or evidence-based mechanisms that have demonstrated researchers’ knowledge, attitudes, or perspectives on ethical issues that impact the development of AI/ML algorithms or how they consider integrating research ethics into their work. Furthermore, how to develop and deliver effective AI ethics education is another issue that requires systematic scientific inquiry. This proposed supplement brings together AI researchers and bioethicists to create the first measure scale to measure medical AI researchers’ attitudes toward AI research principles (beneficence, non-maleficence, justice, and responsibility) and their knowledge about how to use these principles to guide ethical decision making in conducting Alzheimer’s Disease Research using AI through the use of case study vignettes. To create effective AI ethics education geared toward AI AD researchers, we bring in virtual-reality serious game designers to develop a VR-based, interactive application for education on ethical decision-making medical AI in research. Such an interactive and immersive mode of delivering educational materials has been shown to lead to more engagement, enjoyment, and higher effectiveness, compared to traditional educational channels. Information collected from researchers as well as a community advisory board will also inform the development of this AI ethics training program. The usability and effectiveness of the VR application will be evaluated using post-test survey and focus group.