# Medico 2025: Visual Question Answering for Gastrointestinal Imaging

arXiv: https://arxiv.org/abs/2508.10869 · HTML: https://mlsanity.com/paper/2508.10869/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 14 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Multimodal Explanations: Justifying Decisions and Pointing to the Evidence](https://mlsanity.com/paper/1802.08129/) (2018)
- [Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods](https://mlsanity.com/paper/2111.02398/) (2021)
- [Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy](https://mlsanity.com/paper/2011.08065/) (2020)
- [Kvasir-VQA: A Text-Image Pair GI Tract Dataset](https://mlsanity.com/paper/2409.01437/) (2024)
- [Kvasir-VQA-x1: A Multimodal Dataset for Medical Reasoning and Robust MedVQA in Gastrointestinal Endoscopy](https://mlsanity.com/paper/2506.09958/) (2025)

## Built on by (most cited first, 3 of 3)

- [Multi-Task Learning for Visually Grounded Reasoning in Gastrointestinal VQA](https://mlsanity.com/paper/2511.04384/) (2025)
- [Beyond the Leaderboard: Design Lessons for Trustworthy Multimodal VQA](https://mlsanity.com/paper/2607.15241/) (2026)
- [Towards Grounded GI Endoscopy VQA via Multi-Task Learning on Small VLMs](https://mlsanity.com/paper/2607.27122/) (2026)

## Abstract (arXiv)

The Medico 2025 challenge addresses Visual Question Answering (VQA) for Gastrointestinal (GI) imaging, organized as part of the MediaEval task series. The challenge focuses on developing Explainable Artificial Intelligence (XAI) models that answer clinically relevant questions based on GI endoscopy images while providing interpretable justifications aligned with medical reasoning. It introduces two subtasks: (1) answering diverse types of visual questions using the Kvasir-VQA-x1 dataset, and (2) generating multimodal explanations to support clinical decision-making. The Kvasir-VQA-x1 dataset, created from 6,500 images and 159,549 complex question-answer (QA) pairs, serves as the benchmark for the challenge. By combining quantitative performance metrics and expert-reviewed explainability assessments, this task aims to advance trustworthy Artificial Intelligence (AI) in medical image analysis. Instructions, data access, and an updated guide for participation are available in the official competition repository: https://github.com/simula/MediaEval-Medico-2025