# Kvasir-VQA-x1: A Multimodal Dataset for Medical Reasoning and Robust MedVQA in Gastrointestinal Endoscopy arXiv: https://arxiv.org/abs/2506.09958 ยท HTML: https://mlsanity.com/paper/2506.09958/ ## Key facts - Cited by 11 papers in the map (data through 14 Oct 2026). - Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. - Builds on 22 of its 54 references that are in the map. - First posted on arXiv 11 Jun 2025. ## Placed in - LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/) ## Builds on (most cited first, 22 of 22) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021) - [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020) - [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025) - [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023) - [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025) - [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019) - [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022) - [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023) - [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019) - [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021) - [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020) - [Med-Flamingo: a Multimodal Medical Few-shot Learner](https://mlsanity.com/paper/2307.15189/) (2023) - [SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning](https://mlsanity.com/paper/2408.05517/) (2024) - [PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering](https://mlsanity.com/paper/2305.10415/) (2023) - [Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment](https://mlsanity.com/paper/2312.12148/) (2023) - [MedFrameQA: A Multi-Image Medical VQA Benchmark for Clinical Reasoning](https://mlsanity.com/paper/2505.16964/) (2025) - [R-LLaVA: Improving Med-VQA Understanding through Visual Region of Interest](https://mlsanity.com/paper/2410.20327/) (2024) - [Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation Models](https://mlsanity.com/paper/2401.12215/) (2024) - [LaPA: Latent Prompt Assist Model For Medical Visual Question Answering](https://mlsanity.com/paper/2404.13039/) (2024) - [Vision-Language and Large Language Model Performance in Gastroenterology: GPT, Claude, Llama, Phi, Mistral, Gemma, and Quantized Models](https://mlsanity.com/paper/2409.00084/) (2024) - [DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels](https://mlsanity.com/paper/2503.18536/) (2025) ## Built on by (most cited first, 11 of 11) - [HEDGE: Hallucination Estimation via Dense Geometric Entropy for VQA with Vision-Language Models](https://mlsanity.com/paper/2511.12693/) (2025) - [RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology](https://mlsanity.com/paper/2605.10761/) (2026) - [Medico 2025: Visual Question Answering for Gastrointestinal Imaging](https://mlsanity.com/paper/2508.10869/) (2025) - [Multi-Task Learning for Visually Grounded Reasoning in Gastrointestinal VQA](https://mlsanity.com/paper/2511.04384/) (2025) - [Multimodal AI for Gastrointestinal Diagnostics: Tackling VQA in MEDVQA-GI 2025](https://mlsanity.com/paper/2507.14544/) (2025) - [Colon-X: Advancing Intelligent Colonoscopy toward Clinical Reasoning](https://mlsanity.com/paper/2512.03667/) (2025) - [SAGE: An Expert-Annotated South Asian GI Endoscopy Dataset for Multimodal Learning and Hallucination Analysis](https://mlsanity.com/paper/2606.22144/) (2026) - [Querying GI Endoscopy Images: A VQA Approach](https://mlsanity.com/paper/2507.21165/) (2025) - [Beyond the Leaderboard: Design Lessons for Trustworthy Multimodal VQA](https://mlsanity.com/paper/2607.15241/) (2026) - [Measuring and Improving Complex-Atomic Answer Consistency in Endoscopic VQA](https://mlsanity.com/paper/2607.17834/) (2026) - [Towards Grounded GI Endoscopy VQA via Multi-Task Learning on Small VLMs](https://mlsanity.com/paper/2607.27122/) (2026) ## Abstract (arXiv) Medical Visual Question Answering (MedVQA) is a promising field for developing clinical decision support systems, yet progress is often limited by the available datasets, which can lack clinical complexity and visual diversity. To address these gaps, we introduce Kvasir-VQA-x1, a new, large-scale dataset for gastrointestinal (GI) endoscopy. Our work significantly expands upon the original Kvasir-VQA by incorporating 159,549 new question-answer pairs that are designed to test deeper clinical reasoning. We developed a systematic method using large language models to generate these questions, which are stratified by complexity to better assess a model's inference capabilities. To ensure our dataset prepares models for real-world clinical scenarios, we have also introduced a variety of visual augmentations that mimic common imaging artifacts. The dataset is structured to support two main evaluation tracks: one for standard VQA performance and another to test model robustness against these visual perturbations. By providing a more challenging and clinically relevant benchmark, Kvasir-VQA-x1 aims to accelerate the development of more reliable and effective multimodal AI systems for use in clinical settings. The dataset is fully accessible and adheres to FAIR data principles, making it a valuable resource for the wider research community. Code and data: https://github.com/Simula/Kvasir-VQA-x1 and https://huggingface.co/datasets/SimulaMet/Kvasir-VQA-x1