@inproceedings{marimuthu-etal-2026-structured,
    title = "Structured Radiology Intelligence: Extracting Structured Data from {MRI} Reports Using {LLM}s",
    author = "Marimuthu, Sushvin  and
      Krishnamurthy, Parameswari  and
      Sharma, Dipti Misra  and
      H, Goldwin  and
      Eapen, Anu  and
      Simon, Betty  and
      Chandramohan, Anuradha",
    editor = "Gupta, Deepak  and
      Thompson, Paul  and
      Ananiadou, Sophia  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the Third Workshop on Patient-Oriented Language Processing ({CL}4{H}ealth) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/test-year-match/2026.cl4health-1.24/",
    doi = "10.63317/4vob5zztrfso",
    pages = "268--280",
    abstract = "This study presents efforts focused on extracting and structuring doctor notes, specifically Magnetic Resonance Imaging (MRI) reports, into a standardized format using large language models (LLMs). We introduce a novel benchmark dataset comprising of 55 clinically relevant variables given by doctors, making it the first of its kind in the automated processing of unstructured medical texts. The annotations to the dataset were generated using a systematic prompt-tuning approach that was manually validated. It was then evaluated across three experimental stages: baseline, intermediate, and fine-tuned. Each stage assessed the impact of different prompt strategies on the performance of various LLMs (LLaMA, Qwen, and DeepSeek). Among the models tested, LLaMA 3.1 8B Instruct consistently achieved the highest composite Score in both the intermediate and final phases, resulting in an 18.42{\%} improvement in performance."
}