In recent years, the integration of the Digital Twin (DT) concept has followed an upward trend, reflecting a consolidation of research interests in this field. The Digital Twin is applied across a wide range of domains, including medicine, where it contributes to pathology detection and the generation of personalized treatment plans. This article proposes a correlation between the Digital Twin concept and Large Language Models (LLM), as their integration into medical practice facilitates the automation of data analysis processes and enhances the explainability of clinical reasoning related to diagnosis identification and personalized treatment recommendation. The use of these types of Digital Twin involves a series of ethical risks, which vary depending on the category to which they belong—Closed Digital Twin Models or Open-Source Digital Twin Models. This article presents an analysis of the associated ethical risks from a general perspective, focusing on aspects such as data confidentiality, transparency and explainability of decision-making algorithms, and algorithmic bias.