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

• URL: http://arxiv.org/abs/2409.15477v1
• Date: Mon, 23 Sep 2024 18:59:37 GMT
• Title: MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models
• Authors: Mohammad Shahab Sepehri, Zalan Fabian, Maryam Soltanolkotabi, Mahdi Soltanolkotabi,
• Abstract summary: We introduce MediConfusion, a challenging medical Visual Question Answering (VQA) benchmark dataset. 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. 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.
• Score: 20.781551849965357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering [70.44269982045415]
  Retrieval-augmented generation (RAG) has emerged as a promising approach to enhance the performance of large language models (LLMs) We introduce Medical Retrieval-Augmented Generation Benchmark (MedRGB) that provides various supplementary elements to four medical QA datasets. Our experimental results reveals current models' limited ability to handle noise and misinformation in the retrieved documents.
  arXiv  Detail & Related papers  (2024-11-14T06:19:18Z)
• Parameter-Efficient Fine-Tuning Medical Multimodal Large Language Models for Medical Visual Grounding [9.144030136201476]
  Multimodal large language models (MLLMs) inherit the superior text understanding capabilities of LLMs and extend these capabilities to multimodal scenarios. These models achieve excellent results in the general domain of multimodal tasks. However, in the medical domain, the substantial training costs and the requirement for extensive medical data pose challenges to the development of medical MLLMs.
  arXiv  Detail & Related papers  (2024-10-31T11:07:26Z)
• MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models [49.765466293296186]
  Recent progress in Medical Large Vision-Language Models (Med-LVLMs) has opened up new possibilities for interactive diagnostic tools. Med-LVLMs often suffer from factual hallucination, which can lead to incorrect diagnoses. We propose a versatile multimodal RAG system, MMed-RAG, designed to enhance the factuality of Med-LVLMs.
  arXiv  Detail & Related papers  (2024-10-16T23:03:27Z)
• HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale [29.956053068653734]
  We create the PubMedVision dataset with 1.3 million medical VQA samples. Using PubMedVision, we train a 34B medical MLLM HuatuoGPT-Vision, which shows superior performance in medical multimodal scenarios.
  arXiv  Detail & Related papers  (2024-06-27T15:50:41Z)
• CARES: A Comprehensive Benchmark of Trustworthiness in Medical Vision Language Models [92.04812189642418]
  We introduce CARES and aim to evaluate the Trustworthiness of Med-LVLMs across the medical domain. We assess the trustworthiness of Med-LVLMs across five dimensions, including trustfulness, fairness, safety, privacy, and robustness.
  arXiv  Detail & Related papers  (2024-06-10T04:07:09Z)
• Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models [59.60384461302662]
  We introduce Asclepius, a novel benchmark for evaluating Medical Multi-Modal Large Language Models (Med-MLLMs) Asclepius rigorously and comprehensively assesses model capability in terms of distinct medical specialties and different diagnostic capacities. We also provide an in-depth analysis of 6 Med-MLLMs and compare them with 5 human specialists.
  arXiv  Detail & Related papers  (2024-02-17T08:04:23Z)
• OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM [48.16696073640864]
  We introduce OmniMedVQA, a novel comprehensive medical Visual Question Answering (VQA) benchmark. All images in this benchmark are sourced from authentic medical scenarios. We have found that existing LVLMs struggle to address these medical VQA problems effectively.
  arXiv  Detail & Related papers  (2024-02-14T13:51:56Z)
• Large Language Model Distilling Medication Recommendation Model [61.89754499292561]
  We harness the powerful semantic comprehension and input-agnostic characteristics of Large Language Models (LLMs) Our research aims to transform existing medication recommendation methodologies using LLMs. To mitigate this, we have developed a feature-level knowledge distillation technique, which transfers the LLM's proficiency to a more compact model.
  arXiv  Detail & Related papers  (2024-02-05T08:25:22Z)
• Medical Foundation Models are Susceptible to Targeted Misinformation Attacks [3.252906830953028]
  Large language models (LLMs) have broad medical knowledge and can reason about medical information across many domains. We demonstrate a concerning vulnerability of LLMs in medicine through targeted manipulation of just 1.1% of the model's weights. We validate our findings in a set of 1,038 incorrect biomedical facts.
  arXiv  Detail & Related papers  (2023-09-29T06:44:36Z)
• Self-Diagnosis and Large Language Models: A New Front for Medical Misinformation [8.738092015092207]
  We evaluate the capabilities of large language models (LLMs) from the lens of a general user self-diagnosing. We develop a testing methodology which can be used to evaluate responses to open-ended questions mimicking real-world use cases. We reveal that a) these models perform worse than previously known, and b) they exhibit peculiar behaviours, including overconfidence when stating incorrect recommendations.
  arXiv  Detail & Related papers  (2023-07-10T21:28:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.