Imagine sifting through mountains of patient reports, searching for crucial details. That's the daily challenge for medical researchers. Now, a new AI framework offers a powerful solution: automated clinical data extraction. Large Language Models (LLMs), the engines behind AI chatbots, have shown promise in understanding medical text. However, they've had a tendency to 'hallucinate' – generating inaccurate or nonsensical information – particularly in specialized areas like lung lesion analysis. This new research tackles that problem head-on. The researchers have developed a two-stage process that combines the power of LLMs with a 'knowledge-conditioned' approach. Think of it as giving the AI a medical textbook to refer to. In the first stage, the AI identifies potential lung lesions within reports. A key innovation here is the use of an 'internal knowledge base.' This isn't just a static database; it's a dynamic collection of rules generated by the AI itself, based on a small set of training data. To keep these rules accurate, they are constantly cross-checked against an 'external knowledge base' of established medical knowledge. This continuous alignment process helps the AI avoid hallucinations and focus on the most relevant information. The second stage zooms in on the details of each identified lesion. Here, the AI leverages a controlled vocabulary from the SNOMED medical ontology to precisely categorize lesion characteristics. This targeted approach further enhances accuracy. The results are impressive. In tests using real-world clinical trial data, this framework significantly outperformed existing methods, especially in extracting crucial lesion details like size, margins, and solidity. This improvement could revolutionize medical research. By automating data extraction, researchers can quickly analyze vast datasets, uncover hidden patterns, and accelerate the development of new diagnostic and treatment strategies. While the technology is still under development, it points towards a future where AI acts as a powerful assistant to medical professionals, helping them unlock crucial insights from complex patient data and ultimately improving patient care.