mlsanityData to 7 Oct 2026

MeDocVL: A Visual Language Model for Medical Document Understanding and Parsing

arXiv 2602.06402 ↗First posted 6 Feb 2026cs.CVcited by 0

On the map: Multimodal LLMs / vision-language models → Medical vision-language models

Abstract (arXiv)

Medical document OCR is challenging due to complex layouts, domain-specific terminology, and noisy annotations, while requiring strict field-level exact matching. Existing OCR systems and general-purpose vision-language models often fail to reliably parse such documents. We propose MeDocVL, a post-trained vision-language model for query-driven medical document parsing. Our framework combines Training-driven Label Refinement to construct high-quality supervision from noisy annotations, with a Noise-aware Hybrid Post-training strategy that integrates reinforcement learning and supervised fine-tuning to achieve robust and precise extraction. Experiments on medical invoice benchmarks show that MeDocVL consistently outperforms conventional OCR systems and strong VLM baselines, achieving state-of-the-art performance under noisy supervision.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 28 of its 33 references that are in the map. First posted on arXiv 6 Feb 2026.

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28 of 33 references.

References · 28

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16 more
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  1. 2026Dr. DocBench: A Comprehensive Benchmark for Expert-Level and Difficult Document Parsing
  2. 2026Towards Clinically Faithful Medical Image Captioning via Enhanced Vision-Language Alignment
  3. 2026DocOCR-Eval: A Correction-Based Framework for OCR Tool Selection Without Ground Truth
  4. 2023Multi-modal Pre-training for Medical Vision-language Understanding and Generation: An Empirical Study with A New Benchmark
  5. 2025MedVLM-R1: Incentivizing Medical Reasoning Capability of Vision-Language Models (VLMs) via Reinforcement Learning
  6. 2026A Hybrid Architecture for Multi-Stage Claim Document Understanding: Combining Vision-Language Models and Machine Learning for Real-Time Processing
  7. 2025MonkeyOCR v1.5 Technical Report: Unlocking Robust Document Parsing for Complex Patterns
  8. 2026MEDIC-AD: Towards Medical Vision-Language Model's Clinical Intelligence