Ensuring safety in clinical applications of large language models (LLMs) remains an unresolved challenge, particularly for high-risk and underrepresented conditions such as Sickle Cell Disease (SCD). Consequently, these models may exhibit limited reliability for SCD, including hallucinations and clinically unsafe outputs. This paper proposes an LLM-based Multi-Agent System (MAS) enhanced by Retrieval-Augmented Generation (RAG) to support the generation of medical care plans for SCD. The MAS decomposes clinical reasoning into specialized agents responsible for diagnosis, investigation, and treatment planning. Retrieval is framed not as a performance optimization, but as a safety control mechanism. Three RAG strategies, namely LLM-Guided Tree Retrieval, Metadata-Filtered Retrieval, and Semantic Similarity Retrieval, are evaluated alongside a baseline. Our experiments considered LLM-as-a-Judge evaluations and independent assessments by physicians. The results demonstrate high clinical quality, with safety scores exceeding 4 on a 5-point scale. While average performance was similar between RAG and baseline conditions, the Tree Retrieval strategy reduced the frequency of clinically unsafe outputs compared to conventional Semantic Retrieval, indicating fewer clinically unsafe outputs. These findings show evidence that average performance is insufficient to evaluate clinical AI systems, particularly in high-risk scenarios where retrieval serves as a safety control layer.

In this paper, we introduce a new set of lexicons for expressing subjectivity in text documents written in Brazilian Portuguese. Besides the non-English idiom, in contrast to other subjectivity lexicons available, these lexicons represent different subjectivity dimensions (other than sentiment) and are more compact in number of terms. This last feature was designed intentionally to leverage the power of word embedding techniques, i.e., with the words mapped to an embedding space and the appropriate distance measures, we can easily capture semantically related words to the ones in the lexicons. Thus, we do not need to build comprehensive vocabularies and can focus on the most representative words for each lexicon dimension. We showcase the use of these lexicons in three highly non-trivial tasks: (1) Automated Essay Scoring in the Presence of Biased Ratings, (2) Subjectivity Bias in Brazilian Presidential Elections and (3) Fake News Classification Based on Text Subjectivity. All these tasks involve text documents written in Portuguese.