Pain is one of the most common and clinically important symptoms, reflecting disease onset, progression, and treatment response. However, pain is difficult to accurately capture and interpret from medical text, as its expression varies across languages, documentation types, and patient demographics, and is often embedded in terminology, negation, temporality, and contextual descriptions. Existing pain-related studies remain largely monolingual or limited to narrow clinical settings. In this study, we introduce PAINT, a multilingual framework for studying pain representation across medical exams and real-world clinical texts. PAINT includes nine pain-focused dataset partitions spanning English, Spanish, Portuguese, and Japanese, with clinician annotations in all four languages. We curated a structured schema covering pain relevance, pain presence, anatomical location, pain descriptors, and demographic information. To capture multilingual pain representations, we leverage large language models (LLMs) and adopt an ensemble strategy to improve performance. Ten clinicians across four languages validate the labels: the ensemble reaches macro-F1 of 0.91–1.00 on pain relevance and pain presence and 92–100% accuracy on demographics, with inter-annotator κ of 0.82–1.00. We release the WorldMedQA-Pain ensemble labels, the extraction pipeline, validation scripts, and clinician annotation sheets for publicly licensed corpora, providing a foundation for pain-centered clinical NLP and for studying pain documentation across languages and healthcare contexts.

The consultation of reference cases in clinical radiology reporting motivates integrating multimodal retrieval-augmented generation (RAG) into automated systems. However, traditional RAG approaches employ single-vector embeddings for indexing, resulting in loss of granular information. Moreover, a semantic gap exists whereby visually similar radiographs often encode distinct pathologies. In this paper, we propose a Semantics-guided Multi-modal Late Interaction RAG (SeMLI-RAG) framework for radiology report generation. Our approach employs a weight-based late interaction strategy for retrieval to capture fine-grained radiographic features. Furthermore, we introduce an image-adaptive RAG to selectively leverage retrieved information, mitigating performance degradation caused by retrieval-induced noise. Experiments on three radiology benchmarks demonstrate that our framework yields consistent improvements over the backbone and RAG baselines across multiple evaluation dimensions. Further analysis shows that improving retrieval granularity and retrieval robustness can both benefit radiology report generation.

This paper presents the results of the Data Science for Digital Health (DS4DH) group in the MEDIQA-Chat Tasks at ACL-ClinicalNLP 2023. Our study combines the power of a classical machine learning method, Support Vector Machine, for classifying medical dialogues, along with the implementation of one-shot prompts using GPT-3.5. We employ dialogues and summaries from the same category as prompts to generate summaries for novel dialogues. Our findings exceed the average benchmark score, offering a robust reference for assessing performance in this field.