This paper presents an approach to localizing visual answers within continuous medical videos using a multi-step multimodal generation pipeline with the MedGenVidQA dataset. We frame visual answer localization as a multimodal fusion problem, integrating raw video, timestamped ASR transcripts, and VLM-generated scene descriptions into structured contextual blocks, enabling the model to cross-reference spoken commentary against observable physical events. We show that targeted guidance, which forces the model to treat audio transcripts as supplementary hints with observable visual movements, significantly outperforms baseline approaches. It achieves state-of-the-art performance on the test leaderboard, yielding an mIoU of 79.55, alongside IoU@0.3, IoU@0.5, and IoU@0.7 scores of 93.75, 90.00, and 77.50, respectively. Our findings highlight the effectiveness of combining multimodal context fusion with targeted guidance to overcome text bias, establishing a promising approach for achieving the micro-level precision required in the medical domain. We release our code on GitHub at https://github.com/biodatlab/medgenvidqa-lamar.

This paper presents an ensemble of Qwen3.5-4B language models for extracting medical decisions from discharge summaries in the MedDec dataset. The models were trained to annotate discharge summaries with inline XML-like tags. Three different training strategies were used including dynamic fine-tuning, reinforcement learning, and pseudo-label augmentation. By combining predictions based on inter-model agreement, the system improved performance across evaluation metrics, achieving an overall F1 of 0.5942 and ranking second on the test leaderboard. The results also showed stable performance across demographic groups, suggesting fairness for underrepresented populations.

Eligibility criteria (EC) are critical components of clinical trial design, defining the parameters for participant inclusion and exclusion. However, designing EC remains a complex, expertise-intensive process. Traditional approaches to EC generation may fail to produce comprehensive, contextually appropriate criteria. To address these challenges, we introduce EC-RAFT, a method that utilizes Retrieval-Augmented Fine-Tuning (RAFT) to generate structured and cohesive EC directly from clinical trial titles and descriptions. EC-RAFT integrates contextual retrieval, synthesized intermediate reasoning, and fine-tuned language models to produce comprehensive EC sets. To enhance clinical alignment evaluation with referenced criteria, we also propose an LLM-guided evaluation pipeline. Our results demonstrate that our solution, which uses Llama-3.1-8B-Instruct as a base model, achieves a BERTScore of 86.23 and an EC-matched LLM-as-a-Judge score of 1.66 out of 3, outperforming zero-shot Llama-3.1 and Gemini-1.5 by 0.41 and 0.11 points, respectively. On top of that, EC-RAFT also outperforms other fine-tuned versions of Llama-3.1. EC-RAFT was trained in a low-cost setup and, therefore, can be used as a practical solution for EC generation while ensuring quality and relevance in clinical trial design. We release our code on GitHub at https://github.com/biodatlab/ec-raft/

This paper presents an approach to answering patient-specific medical questions using electronic health record (EHR) grounding with ArchEHR-QA 2025 datasets. We address medical question answering as an alignment problem, focusing on generating responses factually consistent with patient-specific clinical notes through in-context learning techniques. We show that LLM-generated responses, used as few-shot examples with GPT-4.1 and Gemini-2.5-Pro, significantly outperform baseline approaches (overall score = 49.1), achieving strict precision, recall, and F1-micro scores of 60.6, 53.6, and 56.9, respectively, on the ArchEHR-QA 2025 test leaderboard. It achieves textual similarity between answers and essential evidence using BLEU, ROUGE, SARI, BERTScore, AlignScore, and MEDCON scores of 6.0, 32.1, 65.8, 36.4, 64.3, and 43.6, respectively. Our findings highlight the effectiveness of combining EHR grounding with few-shot examples for personalized medical question answering, establishing a promising approach for developing accurate and personalized medical question answering systems. We release our code at https://github.com/biodatlab/archehr-qa-lamar.