Imagine an AI that can diagnose diseases and analyze complex medical literature with human-level reasoning capabilities. While this may sound like science fiction, it's a goal driving significant research in artificial intelligence (AI). A critical aspect of this research involves evaluating how well Large Language Models (LLMs), the technology behind chatbots like ChatGPT and Bard, perform in specialized fields like biomedicine. New research in a preprint paper titled "SylloBio-NLI: Evaluating Large Language Models on Biomedical Syllogistic Reasoning" delves into this challenge, exploring whether LLMs can truly grasp the complex logic of medical knowledge. Syllogistic reasoning, the ability to draw conclusions from a set of premises (e.g., if all A are B, and all B are C, then all A are C), is fundamental to human thought and crucial for navigating intricate medical information. The researchers created a framework called SylloBio-NLI to test LLMs across various syllogistic patterns within the context of human biological pathways. The study revealed several intriguing findings. First, it turns out that LLMs, when used out-of-the-box (zero-shot setting), struggle significantly with these biomedical syllogisms. Their accuracy levels were surprisingly low, barely exceeding random guessing in some cases. However, the research also offered a glimmer of hope: providing LLMs with a few examples (few-shot setting) can substantially improve their reasoning abilities. This suggests that LLMs can indeed learn the complex reasoning required in biomedical contexts, but they require guidance. The results varied across different models and the type of syllogisms presented, indicating that some models are naturally more adept at this type of logical thinking than others. A particularly challenging aspect for LLMs was handling variations in how these syllogisms are phrased, highlighting that they are sensitive to nuances in language. This sensitivity suggests that current LLMs, while capable of improvement, might not yet be ready for clinical applications where accurate reasoning is paramount. The findings have implications for how LLMs can be trained and used in the medical field. They point towards a future where AI can assist doctors in diagnosing diseases, researchers in analyzing medical literature, and even patients in understanding their own health conditions. But further research is needed to make AI medical reasoning robust and reliable.