A workflow utilizing general-purpose large language models for efficient structuring and data mining of bone scintigraphy narratives

Abstract

Whole-body bone scintigraphy is pivotal for skeletal evaluation in oncological monitoring, yet the unstructured nature of clinical reports impedes efficient data management and multicenter integration. This study aims to validate whether a clinical-logic-guided prompting framework can effectively constrain general-purpose large language models (LLMs) to achieve reliable structured information extraction from bone scintigraphy narratives without domain-specific fine-tuning, and to evaluate the performance of this workflow in real-world clinical scenarios. We first established a multicenter ground-truth dataset to benchmark four LLMs (DeepSeek-R1, DeepSeek-V3, GPT-o3, and Gemini 2.5 Pro), and DeepSeek-R1 demonstrated the highest accuracy and stability in structured extraction. Subsequently, using DeepSeek-R1, we executed two validation tasks. In a human-in-the-loop workflow, LLM assistance reduced manual processing time by 74.5%-82.6% while significantly enhancing accuracy. Furthermore, we constructed a bone metastasis atlas for eight common malignancies through the automated processing of data from initial 18,331 patients. Our study demonstrates that prompt engineering designed by clinical experts, integrating clinical logic and controlled vocabularies, can effectively guide general-purpose LLMs for bone scintigraphy narrative information extraction. This approach provides a validated low-code paradigm for physicians to transform medical narratives into analyzable structured data, thereby empowering large-scale clinical research.

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