7.2 Future works
7.2.2 Leveraging large language model for text generation for characterization brain and
on vast amounts of textual data, these models demonstrate an impressive ability to understand, generate, and interact using human language. Their architecture and scale enable them to capture contextual meanings from diverse linguistic sources.
LLMs can be applied in multi-modality domains such as the medical imaging domain. For instance, the model is expected to predict brain volume, brain ages, or other brain-related metrics given single thigh slices.
This designed paradigm offers unparalleled advantages. The inherent explainability ensures that generated records are both detailed and contextually relevant, translating intricate visual data into interpretable content.
Their precision ensures no nuanced feature is overlooked, while their versatility enables concurrent process- ing of diverse data forms, such as textual medical histories, for a holistic analysis. LLMs guarantee consistent and rapid record generation. Their adaptability also allows for outputs tailored to varied audiences, and the inherent feedback loop ensures ongoing refinement and accuracy improvement in response to expert input.
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