Imagine a world where AI can analyze medical images like X-rays, helping doctors diagnose diseases more accurately and efficiently. That's the exciting premise behind new research from Weill Cornell Medicine and Thomas Jefferson University Hospital. Traditionally, training powerful AI models for medical image analysis requires huge amounts of labeled data, which is costly and time-consuming to obtain. This research explores a clever workaround: using 'weak labels' generated automatically by simpler AI or rule-based systems to train a smaller, more manageable model called Llama 3.1-8B. The team focused on two tasks: classifying lung diseases from radiology reports (multiple-choice) and extracting disease findings from these reports in a free-form manner (open-ended). Surprisingly, when fine-tuned with automatically generated labels from GPT4-o (a more powerful AI), Llama 3.1-8B achieved near-expert performance in the open-ended task, with a micro-F1 score of 0.91. Even with less accurate labels from a rule-based labeler, the model managed to surpass its 'teacher' in the multiple-choice task, scoring 0.67 compared to the teacher's 0.63! This suggests the student model can learn valuable insights even from imperfect data, much like a bright pupil surpassing a flawed tutor. This breakthrough demonstrates the potential of fine-tuning lightweight LLMs with automatically generated labels, opening doors for cost-effective and accessible AI tools in the medical field. This is particularly relevant for hospitals where deploying massive AI models is challenging due to privacy, financial, and computational constraints. While the research focuses on chest X-rays and simplified tasks, it offers a compelling vision of the future where AI can assist doctors in complex real-world diagnoses, leading to improved patient care.