One of the pillars of healthcare digital transformation focuses on the integration of AI-based dialogue solutions within the clinician-patient relationships with the aim of monitoring and/or supporting them toward the achievement of healthy functional status. The advent of large language models (LLMs) changed the research paradigm in natural language processing (NLP) and it put the basis for new challenges. Two of the most main issues of such LLMs are identified by the analysis of how they are ethical and sustainable. By “ethical”, we mean their capability of both building models that are not biased (in a broader sense) and generating content that is controlled with respect to the task they are used for and the environment in which they are deployed. Instead, as “sustainable”, we mean the possibility of training and fine-tuning LLMs with costs that may be afforded by any academic entity. In this Ph.D., the candidate will investigate (i) how to design dialogue systems in a sustainable way by performing fine-tuning operations on existing LLMs; and, (ii) how to inject grounded knowledge into LLMs to enable ethical and bounded conversations between the AI-based system and the target users.