Knowledge Graph Based Agent for Complex, Knowledge-Intensive QA in Medicine

Biomedical knowledge is uniquely complex and structured, requiring distinct reasoning strategies compared to other scientific disciplines like physics or chemistry. Biomedical scientists do not rely on a single approach to reasoning; instead, they use various strategies, including rule-based, prototype-based, and casebased reasoning. This diversity calls for flexible approaches that accommodate multiple reasoning strategies while leveraging in-domain knowledge. These triplets are then verified against a grounded KG to filter out erroneous information and ensure that only accurate, relevant data contribute to the final answer. Unlike RAG-based models, this multi-step process ensures robustness in reasoning while adapting to different models of medical reasoning. Medical reasoning involves making diagnostic and therapeutic decisions while also understanding the pathology of diseases (Patel et al., 2005). Unlike many other scientific domains, medical reasoning often relies on vertical reasoning, using analogy more heavily (Patel et al., 2005). For instance, in biomedical research, an organism such as Drosophila is used as an exemplar to model a disease mechanism, which is then applied by analogy to other organisms, including humans. In clinical practice, the patient serves as an exemplar, with generalizations drawn from many overlapping disease models and similar patient populations (Charles et al., 1997; Menche et al., 2015). In contrast, fields like physics and chemistry tend to be horizontally organized, where general principles are applied to specific cases (Blois, 1988). This distinction highlights the unique challenges that medical reasoning poses for question-answering (QA) models. While large language models (LLMs) (OpenAI, 2024; Dubey et al., 2024; Gao et al., 2024) have demonstrated strong general capabilities, their responses to medical questions often suffer from incorrect retrieval, missing key information, and misalignment with current scientific and medical knowledge.