Diagnosing medical conditions like anemia often involves a complex series of tests and observations, guided by established clinical guidelines. But what if AI could assist in this process, potentially making diagnosis faster and more efficient? Researchers explored the potential of Large Language Models (LLMs) like GPT-4, LLaMA, and Mistral to generate diagnostic pathways for anemia, mimicking the decision-making process of clinicians. They tested how well these LLMs could suggest the right sequence of lab tests, leading to an accurate diagnosis. The study used a synthetic dataset designed to mirror the real-world complexity of anemia diagnosis. Interestingly, simply providing the LLMs with patient data wasn't enough for accurate diagnoses. However, when researchers incorporated established medical knowledge (in the form of decision tree rules) into the LLMs' prompts, their performance improved significantly. GPT-4 emerged as the top performer, closely mirroring the accuracy of the decision tree itself. LLaMA showed promising results, while Mistral struggled to consistently apply the provided rules. This research highlights the potential of LLMs to assist healthcare professionals by generating diagnostic pathways. However, it also underscores the importance of integrating existing medical knowledge into AI systems to ensure their reliability and effectiveness. Future research will focus on testing these models with real-world patient data and broadening their application to other medical conditions. The potential for AI to play a more significant role in healthcare is becoming increasingly clear. This study is a step toward understanding how LLMs can streamline diagnosis and ultimately improve patient care.