Can Common VLMs Rival Medical VLMs? Evaluation and Strategic Insights

• URL: http://arxiv.org/abs/2506.17337v1
• Date: Thu, 19 Jun 2025 07:59:00 GMT
• Title: Can Common VLMs Rival Medical VLMs? Evaluation and Strategic Insights
• Abstract summary: Medical vision-language models (VLMs) leverage large-scale pretraining for diverse imaging tasks but require substantial computational and data resources.<n>This study systematically evaluates common and medical VLMs across disease diagnosis and visual question answering (VQA)<n>While medical-specific pretraining provides advantages in ID settings, common VLMs match or surpass medical-specific models after lightweight fine-tuning.
• Score: 31.74022063709582
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical vision-language models (VLMs) leverage large-scale pretraining for diverse imaging tasks but require substantial computational and data resources. Meanwhile, common or general-purpose VLMs (e.g., CLIP, LLaVA), though not trained for medical use, show promise with fine-tuning. This raises a key question: Can efficient fine-tuned common VLMs rival generalist medical VLMs for solving specific medical imaging tasks? This study systematically evaluates common and medical VLMs across disease diagnosis and visual question answering (VQA). Using CLIP-based and LLaVA-based models, we examine (1) off-the-shelf performance gaps in in-domain (ID) settings, (2) whether fine-tuning bridges these gaps, and (3) generalization to out-of-domain (OOD) tasks on unseen medical modalities. While medical-specific pretraining provides advantages in ID settings, common VLMs match or surpass medical-specific models after lightweight fine-tuning, with LoRA-based adaptation proving highly effective among different tasks. In OOD tasks, common VLMs demonstrate strong adaptability in some tasks, challenging the assumption that medical-specific pre-training is essential. These findings suggest that leveraging common VLMs with fine-tuning offers a scalable and cost-effective alternative to developing large-scale medical VLMs, providing crucial insights for future research in the medical imaging field.

Related papers

• Aloe-Vision: Robust Vision-Language Models for Healthcare [4.974285692877258]
  Large Vision-Language Models (LVLMs) specialized in healthcare are emerging as a promising research direction.<n>However, progress is constrained by the scarcity of high-quality medical multimodal data, concerns about robustness in safety-critical settings, and the narrow and potentially contaminated evaluation benchmarks.<n>This work introduces Aloe-Vision-Data, a large-scale, quality-filtered mixture which integrates both medical and general domains across multimodal and text-only sources.
  arXiv  Detail & Related papers  (2026-06-25T19:36:38Z)
• Vision Language Models for Dynamic Human Activity Recognition in Healthcare Settings [5.301609879131692]
  Vision Language Models (VLMs) have emerged as promising tools in various healthcare applications.<n>One area that remains relatively underexplored is their use in human activity recognition (HAR) for remote health monitoring.<n>This work contributes a strong benchmark and opens new possibilities for the integration of VLMs into intelligent healthcare systems.
  arXiv  Detail & Related papers  (2025-10-24T13:04:13Z)
• MedAlign: A Synergistic Framework of Multimodal Preference Optimization and Federated Meta-Cognitive Reasoning [52.064286116035134]
  We develop MedAlign, a framework to ensure visually accurate LVLM responses for Medical Visual Question Answering (Med-VQA)<n>We first propose a multimodal Direct Preference Optimization (mDPO) objective to align preference learning with visual context.<n>We then design a Retrieval-Aware Mixture-of-Experts (RA-MoE) architecture that utilizes image and text similarity to route queries to a specialized and context-augmented LVLM.
  arXiv  Detail & Related papers  (2025-10-24T02:11:05Z)
• Small Language Models for Emergency Departments Decision Support: A Benchmark Study [26.333059604118414]
  Large language models (LLMs) have become increasingly popular in medical domains to assist physicians with a variety of clinical and operational tasks.<n>Small language models (SLMs) offer significant potential due to their inherent reasoning capability and efficient performance.<n>This enables SLMs to support physicians by providing timely and accurate information synthesis, thereby improving clinical decision-making and workflow efficiency.
  arXiv  Detail & Related papers  (2025-10-05T04:46:30Z)
• CLARIFY: A Specialist-Generalist Framework for Accurate and Lightweight Dermatological Visual Question Answering [0.5310914438304387]
  We introduce CLARIFY, a Specialist-Generalist framework for dermatological visual question answering (VQA)<n>CLARIFY combines two components: (i) a lightweight, domain-trained image classifier (the Specialist) that provides fast and highly accurate diagnostic predictions, and (ii) a powerful yet compressed conversational VLM (the Generalist) that generates natural language explanations to user queries.<n>Experiments on our curated multimodal dermatology dataset demonstrate that CLARIFY achieves an 18% improvement in diagnostic accuracy over the strongest baseline.
  arXiv  Detail & Related papers  (2025-08-25T19:22:16Z)
• How Far Have Medical Vision-Language Models Come? A Comprehensive Benchmarking Study [16.84832179579428]
  Vision-Language Models (VLMs) trained on web-scale corpora excel at natural image tasks and are increasingly repurposed for healthcare.<n>We present a comprehensive evaluation of open-source general-purpose and medically specialised VLMs, across eight benchmarks.<n>First, large general-purpose models already match or surpass medical-specific counterparts on several benchmarks, demonstrating strong zero-shot transfer from natural to medical images.<n>Second, reasoning performance is consistently lower than understanding, highlighting a critical barrier to safe decision support.
  arXiv  Detail & Related papers  (2025-07-15T11:12:39Z)
• Uncertainty-Driven Expert Control: Enhancing the Reliability of Medical Vision-Language Models [52.2001050216955]
  Existing methods aim to enhance the performance of Medical Vision Language Model (MedVLM) by adjusting model structure, fine-tuning with high-quality data, or through preference fine-tuning.<n>We propose an expert-in-the-loop framework named Expert-Controlled-Free Guidance (Expert-CFG) to align MedVLM with clinical expertise without additional training.
  arXiv  Detail & Related papers  (2025-07-12T09:03:30Z)
• MAM: Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis via Role-Specialized Collaboration [57.98393950821579]
  We introduce the Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis (MAM)<n>Inspired by our empirical findings, MAM decomposes the medical diagnostic process into specialized roles: a General Practitioner, Specialist Team, Radiologist, Medical Assistant, and Director.<n>This modular and collaborative framework enables efficient knowledge updates and leverages existing medical LLMs and knowledge bases.
  arXiv  Detail & Related papers  (2025-06-24T17:52:43Z)
• Challenging Vision-Language Models with Surgical Data: A New Dataset and Broad Benchmarking Study [0.6120768859742071]
  We present the first large-scale study assessing the capabilities of Vision Language Models (VLMs) for endoscopic tasks.<n>Using a diverse set of state-of-the-art models, multiple surgical datasets, and extensive human reference annotations, we address three key research questions.<n>Our results reveal that VLMs can effectively perform basic surgical perception tasks, such as object counting and localization, with performance levels comparable to general domain tasks.
  arXiv  Detail & Related papers  (2025-06-06T16:53:12Z)
• MMedAgent-RL: Optimizing Multi-Agent Collaboration for Multimodal Medical Reasoning [63.63542462400175]
  We propose MMedAgent-RL, a reinforcement learning-based multi-agent framework that enables dynamic, optimized collaboration among medical agents.<n> Specifically, we train two GP agents based on Qwen2.5-VL via RL: the triage doctor learns to assign patients to appropriate specialties, while the attending physician integrates the judgments from multi-specialists.<n>Experiments on five medical VQA benchmarks demonstrate that MMedAgent-RL not only outperforms both open-source and proprietary Med-LVLMs, but also exhibits human-like reasoning patterns.
  arXiv  Detail & Related papers  (2025-05-31T13:22:55Z)
• Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
  Large language models (LLMs) often struggle with open-ended medical questions.<n>We propose a novel approach utilizing structured medical reasoning.<n>Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
  arXiv  Detail & Related papers  (2025-03-05T05:24:55Z)
• Improving Medical Large Vision-Language Models with Abnormal-Aware Feedback [57.98393950821579]
  We propose a novel UMed-LVLM designed to unveil medical abnormalities.<n>We propose a prompt method utilizing the GPT-4V to generate diagnoses based on identified abnormal areas in medical images.<n>Our UMed-LVLM significantly outperforms existing Med-LVLMs in identifying and understanding medical abnormalities.
  arXiv  Detail & Related papers  (2025-01-02T17:37:20Z)
• LLM-MedQA: Enhancing Medical Question Answering through Case Studies in Large Language Models [18.6994780408699]
  Large Language Models (LLMs) face significant challenges in medical question answering.<n>We propose a novel approach incorporating similar case generation within a multi-agent medical question-answering system.<n>Our method capitalizes on the model's inherent medical knowledge and reasoning capabilities, eliminating the need for additional training data.
  arXiv  Detail & Related papers  (2024-12-31T19:55:45Z)
• VILA-M3: Enhancing Vision-Language Models with Medical Expert Knowledge [36.67150853785481]
  Generalist vision language models (VLMs) have made significant strides in computer vision, but they fall short in specialized fields like healthcare.<n>In traditional computer vision tasks, creative or approximate answers may be acceptable, but in healthcare, precision is paramount.<n>This paper introduces a new framework, VILA-M3, for medical VLMs that utilizes domain knowledge via expert models.
  arXiv  Detail & Related papers  (2024-11-19T22:59:14Z)
• GMAI-MMBench: A Comprehensive Multimodal Evaluation Benchmark Towards General Medical AI [67.09501109871351]
  Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals. GMAI-MMBench is the most comprehensive general medical AI benchmark with well-categorized data structure and multi-perceptual granularity to date. It is constructed from 284 datasets across 38 medical image modalities, 18 clinical-related tasks, 18 departments, and 4 perceptual granularities in a Visual Question Answering (VQA) format.
  arXiv  Detail & Related papers  (2024-08-06T17:59:21Z)
• STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-Answering [58.79671189792399]
  STLLaVA-Med is designed to train a policy model capable of auto-generating medical visual instruction data. We validate the efficacy and data efficiency of STLLaVA-Med across three major medical Visual Question Answering (VQA) benchmarks.
  arXiv  Detail & Related papers  (2024-06-28T15:01:23Z)
• Knowledge-grounded Adaptation Strategy for Vision-language Models: Building Unique Case-set for Screening Mammograms for Residents Training [5.819704618007536]
  A visual-language model (VLM) pre-trained on natural images and text pairs poses a significant barrier when applied to medical contexts. We propose a framework designed to adeptly tailor VLMs to the medical domain, employing selective sampling and hard-negative mining techniques.
  arXiv  Detail & Related papers  (2024-05-30T04:04:36Z)
• 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)
• MISS: A Generative Pretraining and Finetuning Approach for Med-VQA [16.978523518972533]
  We propose a large-scale MultI-task Self-Supervised learning based framework (MISS) for medical VQA tasks. We unify the text encoder and multimodal encoder and align image-text features through multi-task learning. Our method achieves excellent results with fewer multimodal datasets and demonstrates the advantages of generative VQA models.
  arXiv  Detail & Related papers  (2024-01-10T13:56:40Z)
• A Survey of Large Language Models in Medicine: Progress, Application, and Challenge [85.09998659355038]
  Large language models (LLMs) have received substantial attention due to their capabilities for understanding and generating human language. This review aims to provide a detailed overview of the development and deployment of LLMs in medicine.
  arXiv  Detail & Related papers  (2023-11-09T02:55:58Z)
• Towards Medical Artificial General Intelligence via Knowledge-Enhanced Multimodal Pretraining [121.89793208683625]
  Medical artificial general intelligence (MAGI) enables one foundation model to solve different medical tasks. We propose a new paradigm called Medical-knedge-enhanced mulTimOdal pretRaining (MOTOR)
  arXiv  Detail & Related papers  (2023-04-26T01:26:19Z)

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.