# MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models

arXiv: https://arxiv.org/abs/2409.15477 · HTML: https://mlsanity.com/paper/2409.15477/

## Key facts

- Cited by 23 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 42 of its 51 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Placed in

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

## Builds on (most cited first, 30 of 42)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [Scaling Laws for Reward Model Overoptimization](https://mlsanity.com/paper/2210.10760/) (2022)
- [Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs](https://mlsanity.com/paper/2401.06209/) (2024)
- [Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding](https://mlsanity.com/paper/2311.16922/) (2023)
- [When and why vision-language models behave like bags-of-words, and what to do about it?](https://mlsanity.com/paper/2210.01936/) (2022)
- [Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality](https://mlsanity.com/paper/2204.03162/) (2022)
- [BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs](https://mlsanity.com/paper/2303.00915/) (2023)
- [PathVQA: 30000+ Questions for Medical Visual Question Answering](https://mlsanity.com/paper/2003.10286/) (2020)
- [SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical Visual Question Answering](https://mlsanity.com/paper/2102.09542/) (2021)
- [Med-Flamingo: a Multimodal Medical Few-shot Learner](https://mlsanity.com/paper/2307.15189/) (2023)
- [PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering](https://mlsanity.com/paper/2305.10415/) (2023)
- [A Survey on Multimodal Large Language Models for Autonomous Driving](https://mlsanity.com/paper/2311.12320/) (2023)
- [Detecting and Preventing Hallucinations in Large Vision Language Models](https://mlsanity.com/paper/2308.06394/) (2023)
- [What's "up" with vision-language models? Investigating their struggle with spatial reasoning](https://mlsanity.com/paper/2310.19785/) (2023)
- [LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models](https://mlsanity.com/paper/2306.09265/) (2023)
- [PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents](https://mlsanity.com/paper/2303.07240/) (2023)
- [OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM](https://mlsanity.com/paper/2402.09181/) (2024)

## Built on by (most cited first, 23 of 23)

- [R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science](https://mlsanity.com/paper/2505.14738/) (2025)
- [UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models](https://mlsanity.com/paper/2504.13109/) (2025)
- [EndoBench: A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysis](https://mlsanity.com/paper/2505.23601/) (2025)
- [3D Shape Tokenization via Latent Flow Matching](https://mlsanity.com/paper/2412.15618/) (2024)
- [MedLA: A Logic-Driven Multi-Agent Framework for Complex Medical Reasoning with Large Language Models](https://mlsanity.com/paper/2509.23725/) (2025)
- [VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate](https://mlsanity.com/paper/2504.12259/) (2025)
- [PET2Rep: Towards Vision-Language Model-Drived Automated Radiology Report Generation for Positron Emission Tomography](https://mlsanity.com/paper/2508.04062/) (2025)
- [Hyperphantasia: A Benchmark for Evaluating the Mental Visualization Capabilities of Multimodal LLMs](https://mlsanity.com/paper/2507.11932/) (2025)
- [Domain Specific Benchmarks for Evaluating Multimodal Large Language Models](https://mlsanity.com/paper/2506.12958/) (2025)
- [AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy](https://mlsanity.com/paper/2510.00063/) (2025)
- [HARMONY: Hidden Activation Representations and Model Output-Aware Uncertainty Estimation for Vision-Language Models](https://mlsanity.com/paper/2510.22171/) (2025)
- [Multi-human Interactive Talking Dataset](https://mlsanity.com/paper/2508.03050/) (2025)
- [Vision-language models for chest radiography do not always need the image](https://mlsanity.com/paper/2606.17710/) (2026)
- [MedBLINK: Probing Basic Perception in Multimodal Language Models for Medicine](https://mlsanity.com/paper/2508.02951/) (2025)
- [Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models](https://mlsanity.com/paper/2601.06750/) (2026)
- [AMVICC: A Novel Benchmark for Cross-Modal Failure Mode Profiling for VLMs and IGMs](https://mlsanity.com/paper/2601.17037/) (2026)
- [MultiMedEdit: A Scenario-Aware Benchmark for Evaluating Knowledge Editing in Medical VQA](https://mlsanity.com/paper/2508.07022/) (2025)
- [From Events to Clarity: The Event-Guided Diffusion Framework for Dehazing](https://mlsanity.com/paper/2511.11944/) (2025)
- [DoubleTake: Contrastive Reasoning for Faithful Decision-Making in Medical Imaging](https://mlsanity.com/paper/2602.02894/) (2026)
- [Do Medical Vision Models Reason About Anatomy? Probing the Spatial Inductive Biases of Learned Visual Representations](https://mlsanity.com/paper/2608.28092/) (2026)
- [Beyond Benchmark Scores: Auditing Medical Vision-Language Models for Chest X-Ray Tuberculosis Screening](https://mlsanity.com/paper/2609.21763/) (2026)
- [Applying Language Models in Clinical Medicine: Recent Trends and Perspectives](https://mlsanity.com/paper/2609.34780/) (2026)
- [Reliability Stress Tests and Decision-Time Routing for Chest X-ray Vision-Language Models](https://mlsanity.com/paper/2610.02270/) (2026)

## Abstract (arXiv)

Multimodal Large Language Models (MLLMs) have tremendous potential to improve the accuracy, availability, and cost-effectiveness of healthcare by providing automated solutions or serving as aids to medical professionals. Despite promising first steps in developing medical MLLMs in the past few years, their capabilities and limitations are not well-understood. Recently, many benchmark datasets have been proposed that test the general medical knowledge of such models across a variety of medical areas. However, the systematic failure modes and vulnerabilities of such models are severely underexplored with most medical benchmarks failing to expose the shortcomings of existing models in this safety-critical domain. In this paper, we introduce MediConfusion, a challenging medical Visual Question Answering (VQA) benchmark dataset, that probes the failure modes of medical MLLMs from a vision perspective. We reveal that state-of-the-art models are easily confused by image pairs that are otherwise visually dissimilar and clearly distinct for medical experts. Strikingly, all available models (open-source or proprietary) achieve performance below random guessing on MediConfusion, raising serious concerns about the reliability of existing medical MLLMs for healthcare deployment. We also extract common patterns of model failure that may help the design of a new generation of more trustworthy and reliable MLLMs in healthcare.