Extracting structured clinical data from unstructured patient transcripts is challenging due to large target schemas and inherent linguistic ambiguity. We address the extraction of 193 heterogeneous clinical attributes from nursing notes and clinician–patient dialogues, and demonstrate that zero-shot large language models (LLMs) are ineffective in this setting, achieving an F1 score below 0.15 due to context window saturation and hallucination. We propose a four-stage framework that combines semantic schema clustering, role-based chain-of-thought prompting, supervised fine-tuning of Llama-3.1-8B, and transcript-verified post-processing. Our approach achieves an F1 score of 0.66, representing a 4.4x improvement over the baseline, by balancing high recall from generative models with high precision from verification. These results highlight the effectiveness of hybrid pipelines for high-stakes clinical information extraction.

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.

Healthcare community question-answering (CQA) forums have become popular for users seeking medical advice, offering answers that range from personal experiences to factual information. Traditionally, CQA summarization relies on the best-voted answer as a reference summary. However, this approach overlooks the diverse perspectives across multiple responses. Structuring summaries by perspective could better meet users’ informational needs. The PerAnsSumm shared task addresses this by identifying and classifying perspective-specific spans (Task_A) and generating perspective-specific summaries from question-answer threads (Task_B). In this paper, we present our work on the PerAnsSumm shared task 2025 at the CL4Health Workshop, NAACL 2025. Our system leverages the RoBERTa-large model for identifying perspective-specific spans and the BART-large model for summarization. We achieved a Macro-F1 score of 0.9 (90%) and a Weighted-F1 score of 0.92 (92%) for classification. For span matching, our strict matching F1 score was 0.21 (21%), while proportional matching reached 0.68 (68%), resulting in an average Task A score of 0.6 (60%). For Task B, we achieved a ROUGE-1 score of 0.4 (40%), ROUGE-2 of 0.18 (18%), and ROUGE-L of 0.36 (36%). Additionally, we obtained a BERTScore of 0.84 (84%), METEOR of 0.37 (37%), and BLEU of 0.13 (13%), resulting in an average Task B score of 0.38 (38%). Combining both tasks, our system achieved an overall average score of 49% and ranked 6th on the official leaderboard for the shared task.

In this paper, we present our work to the MEDIQA-M3G 2024 shared task, which tackles multilingual and multimodal medical answer generation. Our system consists of a lightweight Vision-and-Language Transformer (ViLT) model which is fine-tuned for the clinical dermatology visual question-answering task. In the official leaderboard for the task, our system ranks 6th. After the challenge, we experiment with training the ViLT model on more data. We also explore the capabilities of large Vision-Language Models (VLMs) such as Gemini and LLaVA.