Forming oral models capable of understanding the complete dynamics of the oral cavity is vital across research areas such as speech correction, designing foods for the aging population, and dentistry. Magnetic resonance imaging (MRI) technologies, capable of capturing oral data essential for creating such detailed representations, offer a powerful tool for illustrating articulatory dynamics. However, its real-time application is hindered by expense and expertise requirements. Ever advancing generative AI approaches present themselves as a way to address this barrier by leveraging multi-modal approaches for generating pseudo-MRI views. Nonetheless, this immediately sparks ethical concerns regarding the utilisation of a technology with the capability to produce MRIs from facial observations. This paper explores the ethical implications of external-to-internal correlation modeling (E2ICM). E2ICM utilises facial movements to infer internal configurations and provides a cost-effective supporting technology for MRI. In this preliminary work, we employ Pix2PixGAN to generate pseudo-MRI views from external articulatory data, demonstrating the feasibility of this approach. Ethical considerations concerning privacy, consent, and potential misuse, which are fundamental to our examination of this innovative methodology, are discussed as a result of this experimentation.

翻译：构建能够理解口腔完整动力学的口腔模型，在言语矫正、为老年人群设计食品以及牙科学等多个研究领域至关重要。磁共振成像（MRI）技术能够捕获创建此类详细表征所需的关键口腔数据，为阐明发音动力学提供了有力工具。然而，其实际应用因成本高昂和专业要求而受到限制。不断发展的生成式人工智能方法通过利用多模态方法生成伪MRI视图，为解决这一障碍提供了可能。尽管如此，这项能够通过面部观察生成MRI的技术立即引发了伦理担忧。本文探讨了外部-内部关联建模（E2ICM）的伦理影响。E2ICM利用面部运动推断内部构型，为MRI提供了一种经济高效的支持技术。在这项初步工作中，我们采用Pix2PixGAN从外部发音数据生成伪MRI视图，证明了该方法的可行性。实验引发的关于隐私、知情同意和潜在滥用的伦理考量，构成了我们对这一创新方法进行审视的核心内容。