A Deep Dive into GPT-4's Data Mining Capabilities for Free-Text Spine Radiology Reports

Klaudia Szabó Ledenyi, András Kicsi, László Vidács

Abstract: The significant growth of large language models revolutionized the field of natural language processing. Recent advancements in large language models, particularly generative pretrained transformer (GPT) models, have shown advanced capabilities in natural language understanding and reasoning. These models typically interact with users through prompts rather than providing training data or fine-tuning, which can save a significant amount of time and resources. This paper presents a study evaluating GPT-4’s performance in data mining from free-text spine radiology reports using a single prompt. The evaluation includes sentence classification, sentence-level sentiment analysis and two representative biomedical information extraction tasks: named entity recognition and relation extraction. Our research findings indicate that GPT-4 performs effectively in few-shot information extraction from radiology text, even without specific training for the clinical domain. This approach shows potent