Diagnosing illness is a complex puzzle, requiring doctors to piece together symptoms, medical history, and crucial lab results. Could AI step in and help solve these puzzles? New research explores how large language models (LLMs) perform at generating differential diagnoses—lists of possible illnesses—when given clinical case information, with and without lab test results. The results are intriguing. Researchers tested five LLMs, including GPT-4, GPT-3.5, Llama-2, Claude-2, and Mixtral, on 50 real clinical cases. They found that providing lab data significantly boosted the models' ability to provide accurate diagnoses. GPT-4 performed best, achieving up to 80% “lenient accuracy” when given lab results. This means that while it didn't always pinpoint the *exact* diagnosis as the top choice, it consistently included the correct illness in its wider list of possibilities. Interestingly, the study showed that even open-source LLMs saw improvement when given lab data, suggesting that this type of information is key for AI diagnostic tools. The models successfully interpreted common lab tests like liver function panels and toxicology reports, demonstrating an ability to understand and apply this data. However, while promising, the research also highlights challenges. The models struggled more with complex cases requiring specialized tests, demonstrating that human expertise is still essential. This study suggests a future where AI acts as a powerful assistant to doctors, helping them quickly narrow down possibilities and potentially improving diagnostic speed and accuracy. But further development and refinement are needed before these AI tools become a regular part of healthcare.