Imagine a world where AI could help doctors predict a lung cancer patient's prognosis with remarkable accuracy, even without mountains of patient data. That's the tantalizing possibility hinted at in a recent study exploring the power of large language models (LLMs) like GPT in lung cancer prognosis prediction. Researchers put LLMs like GPT-4o-mini and GPT-3.5 to the test, asking them to predict both patient survival time (1, 2, 3, 4, and 5 years) and the likelihood of post-operative complications. They compared the performance of these LLMs to standard logistic regression models, a traditional statistical method. The results? LLMs held their own, even outperforming traditional models in several survival prediction tasks. Notably, the GPT-4o-mini consistently delivered impressive accuracy, particularly in predicting short and long-term survival. This is particularly exciting because LLMs don’t need access to vast troves of past patient data to make predictions. They rely on the immense medical knowledge absorbed during their training on massive text datasets. This makes them potentially valuable in situations where patient data is scarce or unavailable. While traditional methods often struggle without lots of data, LLMs can still offer helpful insights, much like an experienced doctor drawing upon their extensive knowledge. The ability of LLMs to explain their reasoning is another big plus. This transparency is key for building trust among clinicians and could pave the way for integrating these powerful AI tools into clinical decision-making workflows. However, the study also highlighted some challenges. While the LLMs shone in lung cancer prediction, their performance might not be as stellar across all medical domains. The knowledge embedded within these models isn't uniform across all diseases and conditions, which could affect their prediction accuracy. Plus, researchers couldn't incorporate image data like pathology slides into the analysis – a crucial piece of the puzzle in lung cancer prognosis. Future research aims to integrate such image data to further refine LLM predictions. This research opens a fascinating window into the future of AI in healthcare. While not a replacement for doctors, LLMs could become invaluable tools, augmenting clinical expertise and helping provide personalized, data-driven insights for lung cancer patients.