Papers with Med-VQA
Act as you think: Reinforcing Consistent Reasoning in Medical Visual Question Answering (2026.acl-long)
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Songtao Jiang, Yuan Wang, Ruizhe Chen, Yan Zhang, Ruilin Luo, Bohan Lei, Yeying Jin, Sibo Song, ZhiBo Yang, Jimeng Sun, Jian Wu, Zuozhu Liu
| Challenge: | Recent advances have improved the accuracy of medical visual question answering (Med-VQA) however, the high stakes nature of the medical domain has precipitated a shift towards interpretability and transparency of reasoning processes. |
| Approach: | They propose a reinforcement learning from verifiable rewards framework that rewards internal consistency and logical coherence. |
| Outcome: | The proposed framework rewards internal consistency and logical coherence, and is highly versatile, the authors show. |
Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models (2024.findings-emnlp)
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| Challenge: | Recent advances in multimodal large language models have seen remarkable progress for medical decision-making, however, they are designated for specific classification or generative tasks and require model training or finetuning on large-scale datasets with sizeable parameters and tremendous computing. |
| Approach: | They propose a framework that tackles discriminative and generative multimodal medical tasks using multimodal alignment, instruction tuning and routing. |
| Outcome: | The proposed model can achieve superior performance to or on par with state-of-the-art baselines while only requiring 30%-50% of activated model parameters. |
Multi-modal Concept Alignment Pre-training for Generative Medical Visual Question Answering (2024.findings-acl)
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| Challenge: | Medical Visual Question Answering (Med-VQA) aims to provide accurate answers to questions regarding medical images, a task particularly challenging for open-ended questions. |
| Approach: | They propose a multi-modal concept alignment pre-training approach for generative Med-VQA that leverages a knowledge graph sourced from medical image-caption datasets and the Unified Medical Language System. |
| Outcome: | The proposed approach significantly outperforms existing methods on a set of benchmark datasets and shows high efficiency and knowledge-image alignment capability. |
HSCR: Hierarchical Self-Contrastive Rewarding for Aligning Medical Vision Language Models (2025.acl-long)
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| Challenge: | Existing methods for medical vision-language models overlook modality misalignment . HSCR generates high-quality preference data with higher sampling probability . |
| Approach: | They propose a hierarchical self-contrastive reward approach that addresses two challenges in alignment . they leverage the inherent capability of Med-VLMs to generate dispreferred responses . |
| Outcome: | The proposed approach improves accuracy and trustworthiness of medical vision-label models with 2,000 training entries. |
Worse than Random? An Embarrassingly Simple Probing Evaluation of Large Multimodal Models in Medical VQA (2025.findings-acl)
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| Challenge: | Large Multimodal Models (LMMs) have demonstrated impressive performance on existing medical visual question answering benchmarks. |
| Approach: | They evaluate large multimodal models that perform worse than random guessing on medical questions . authors suggest more robust evaluation methods to ensure reliability of LMMs . |
| Outcome: | a new study shows that large multimodal models perform worse than random guessing on medical visual question answering benchmarks. |
AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering (2025.findings-emnlp)
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| Challenge: | Existing Med-MLLMs fail when deployed in low-resource settings where abundant labeled data is unavailable. |
| Approach: | They propose a training-free agentic framework that performs medical knowledge augmentation via LLM agents. |
| Outcome: | The proposed framework performs medical knowledge augmentation via LLM agents. |