# Clinical Cognition Alignment for Gastrointestinal Diagnosis with Multimodal LLMs

arXiv: https://arxiv.org/abs/2603.20698 · HTML: https://mlsanity.com/paper/2603.20698/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Medical vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 16 of its 61 references that are in the map.
- First posted on arXiv 21 Mar 2026.

## Placed in

- Multimodal LLMs / vision-language models / [Medical vision-language models](https://mlsanity.com/mllm/medical-vision-language-models/)

## Builds on (most cited first, 16 of 16)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning](https://mlsanity.com/paper/2506.07044/) (2025)
- [HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation](https://mlsanity.com/paper/2502.09838/) (2025)
- [Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding](https://mlsanity.com/paper/2510.08668/) (2025)
- [MiniGPT-Med: Large Language Model as a General Interface for Radiology Diagnosis](https://mlsanity.com/paper/2407.04106/) (2024)
- [BiMediX2: Bio-Medical EXpert LMM for Diverse Medical Modalities](https://mlsanity.com/paper/2412.07769/) (2024)
- [Chiron-o1: Igniting Multimodal Large Language Models towards Generalizable Medical Reasoning via Mentor-Intern Collaborative Search](https://mlsanity.com/paper/2506.16962/) (2025)
- [LLM-RadJudge: Achieving Radiologist-Level Evaluation for X-Ray Report Generation](https://mlsanity.com/paper/2404.00998/) (2024)
- [MedMO: Grounding and Understanding Multimodal Large Language Model for Medical Images](https://mlsanity.com/paper/2602.06965/) (2026)
- [EndoBench: A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysis](https://mlsanity.com/paper/2505.23601/) (2025)
- [UniMedVL: Unifying Medical Multimodal Understanding and Generation through Observation-Knowledge-Analysis](https://mlsanity.com/paper/2510.15710/) (2025)
- [Fleming-VL: Towards Universal Medical Visual Reasoning with Multimodal LLMs](https://mlsanity.com/paper/2511.00916/) (2025)
- [ShizhenGPT: Towards Multimodal LLMs for Traditional Chinese Medicine](https://mlsanity.com/paper/2508.14706/) (2025)
- [MediX-R1: Open Ended Medical Reinforcement Learning](https://mlsanity.com/paper/2602.23363/) (2026)
- [Domain-Adaptive Pre-training of Self-Supervised Foundation Models for Medical Image Classification in Gastrointestinal Endoscopy](https://mlsanity.com/paper/2410.21302/) (2024)

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

- [MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution](https://mlsanity.com/paper/2604.26283/) (2026)
- [Compatibility-Aware Dynamic Fine-Tuning for Large Language Models](https://mlsanity.com/paper/2606.11206/) (2026)
- [CARE: Causally-Aligned Reasoning Exploration for Medical Large Language Models](https://mlsanity.com/paper/2608.26147/) (2026)

## Abstract (arXiv)

Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in medical image analysis. However, their application in gastrointestinal endoscopy is currently hindered by two critical limitations: the misalignment between general model reasoning and standardized clinical cognitive pathways, and the lack of causal association between visual features and diagnostic outcomes. In this paper, we propose a novel Clinical-Cognitive-Aligned (CogAlign) framework to address these challenges. First, we endow the model with rigorous clinical analytical capabilities by constructing the hierarchical clinical cognition dataset and employing Supervised Fine-Tuning (SFT). Unlike conventional approaches, this strategy internalizes the hierarchical diagnostic logic of experts, ranging from anatomical localization and morphological evaluation to microvascular analysis, directly into the model. Second, to eliminate visual bias, we provide a theoretical analysis demonstrating that standard supervised tuning inevitably converges to spurious background correlations. Guided by this insight, we propose a counterfactual-driven reinforcement learning strategy to enforce causal rectification. By generating counterfactual normal samples via lesion masking and optimizing through clinical-cognition-centric rewards, we constrain the model to strictly ground its diagnosis in causal lesion features. Extensive experiments demonstrate that our approach achieves State-of-the-Art (SoTA) performance across multiple benchmarks, significantly enhancing diagnostic accuracy in complex clinical scenarios.