  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.
