{"total":101,"rows":[{"id":"2511.22055","title":"OralGPT-Omni: A Versatile Dental Multimodal Large Language Model","created":"2025-11-27","cited_by_count":11,"area":"mllm"},{"id":"2511.00846","title":"OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks","created":"2025-11-02","cited_by_count":8,"area":"mllm"},{"id":"2602.23363","title":"MediX-R1: Open Ended Medical Reinforcement Learning","created":"2026-02-26","cited_by_count":7,"area":"mllm"},{"id":"2602.12705","title":"MedXIAOHE: A Comprehensive Recipe for Building Medical MLLMs","created":"2026-02-13","cited_by_count":6,"area":"mllm"},{"id":"2603.25155","title":"Photon: Speedup Volume Understanding with Efficient Multimodal Large Language Models","created":"2026-03-26","cited_by_count":6,"area":"mllm"},{"id":"2512.16201","title":"Visual Alignment of Medical Vision-Language Models for Grounded Radiology Report Generation","created":"2025-12-18","cited_by_count":5,"area":"mllm"},{"id":"2601.06847","title":"MedGround: Bridging the Evidence Gap in Medical Vision-Language Models with Verified Grounding Data","created":"2026-01-11","cited_by_count":5,"area":"mllm"},{"id":"2509.23379","title":"CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding","created":"2025-09-27","cited_by_count":4,"area":"mllm"},{"id":"2511.14900","title":"Skin-R1: Clinical Knowledge-Guided Dermatological Diagnosis Using Vision-Language Models","created":"2025-11-18","cited_by_count":4,"area":"mllm"},{"id":"2512.10691","title":"Enhancing Radiology Report Generation and Visual Grounding using Reinforcement Learning","created":"2025-12-11","cited_by_count":4,"area":"mllm"},{"id":"2606.23888","title":"E-MRL: Cross-view Aligned Evidence-driven Multimodal Reinforcement Learning for Reliable 3D Tumor Analysis","created":"2026-06-22","cited_by_count":4,"area":"mllm"},{"id":"2601.02737","title":"Unveiling and Bridging the Functional Perception Gap in MLLMs: Atomic Visual Alignment and Hierarchical Evaluation via PET-Bench","created":"2026-01-06","cited_by_count":3,"area":"mllm"},{"id":"2606.06696","title":"MMBU: A Massive Multi-modal Biomedical Understanding Benchmark to Probe the Perception Capabilities of Vision-Language Models","created":"2026-06-04","cited_by_count":3,"area":"mllm"},{"id":"2607.24743","title":"ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding","created":"2026-07-27","cited_by_count":3,"area":"mllm"},{"id":"2607.28318","title":"PathView-Bench: Can Multimodal Large Language Models Achieve Fine-grained Multiscale Understanding of Pathology Images?","created":"2026-07-30","cited_by_count":3,"area":"mllm"},{"id":"2510.03978","title":"No Tokens Wasted: Leveraging Long Context in Biomedical Vision-Language Models","created":"2025-10-04","cited_by_count":2,"area":"mllm"},{"id":"2510.25867","title":"Synthesizing High-Quality Visual Question Answering from Medical Documents with Generator-Verifier LMMs","created":"2025-10-29","cited_by_count":2,"area":"mllm"},{"id":"2511.09195","title":"Towards Trustworthy Dermatology MLLMs: A Benchmark and Multimodal Evaluator for Diagnostic Narratives","created":"2025-11-12","cited_by_count":2,"area":"mllm"},{"id":"2511.22232","title":"From Compound Figures to Composite Understanding: Developing a Multi-Modal LLM from Biomedical Literature with Medical Multiple-Image Benchmarking and Validation","created":"2025-11-27","cited_by_count":2,"area":"mllm"},{"id":"2511.22256","title":"UMind-VL: A Generalist Ultrasound Vision-Language Model for Unified Grounded Perception and Comprehensive Interpretation","created":"2025-11-27","cited_by_count":2,"area":"mllm"},{"id":"2601.18240","title":"V-Loop: Visual Logical Loop Verification for Hallucination Detection in Medical Visual Question Answering","created":"2026-01-26","cited_by_count":2,"area":"mllm"},{"id":"2602.22098","title":"Brain3D: Brain Report Automation via Inflated Vision Transformers in 3D","created":"2026-02-25","cited_by_count":2,"area":"mllm"},{"id":"2603.23501","title":"MedObvious: Exposing the Medical Moravec's Paradox in VLMs via Clinical Triage","created":"2026-03-24","cited_by_count":2,"area":"mllm"},{"id":"2604.08941","title":"Predictive Entropy as a Joint Screen for Error and Paraphrase Instability in Medical Vision-Language Models","created":"2026-04-10","cited_by_count":2,"area":"mllm"},{"id":"2605.10850","title":"Verification Mirage: Mapping the Reliability Boundary of Self-Verification in Medical VQA","created":"2026-05-11","cited_by_count":2,"area":"mllm"},{"id":"2606.02809","title":"Automated Report-Derived Oncology VQA Benchmark for Evaluating Vision-Language Models on 3D Medical Imaging","created":"2026-06-01","cited_by_count":2,"area":"mllm"},{"id":"2606.03180","title":"GLINT: Sparsely Gated Vision-Language Alignment for Fine-Grained Radiology Representations","created":"2026-06-02","cited_by_count":2,"area":"mllm"},{"id":"2606.17710","title":"Vision-language models for chest radiography do not always need the image","created":"2026-06-16","cited_by_count":2,"area":"mllm"},{"id":"2607.11175","title":"The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy","created":"2026-07-13","cited_by_count":2,"area":"mllm"},{"id":"2601.03915","title":"HemBLIP: A Vision-Language Model for Interpretable Leukemia Cell Morphology Analysis","created":"2026-01-07","cited_by_count":1,"area":"mllm"},{"id":"2601.06750","title":"Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models","created":"2026-01-11","cited_by_count":1,"area":"mllm"},{"id":"2601.15408","title":"CURE: Curriculum-guided Multi-task Training for Reliable Anatomy Grounded Report Generation","created":"2026-01-21","cited_by_count":1,"area":"mllm"},{"id":"2603.11625","title":"MedPruner: Training-Free Hierarchical Token Pruning for Efficient 3D Medical Image Understanding in Vision-Language Models","created":"2026-03-12","cited_by_count":1,"area":"mllm"},{"id":"2603.16781","title":"IOSVLM: A 3D Vision-Language Model for Unified Dental Diagnosis from Intraoral Scans","created":"2026-03-17","cited_by_count":1,"area":"mllm"},{"id":"2603.20314","title":"VGS-Decoding: Visual Grounding Score Guided Decoding for Hallucination Mitigation in Medical VLMs","created":"2026-03-19","cited_by_count":1,"area":"mllm"},{"id":"2603.26008","title":"FairLLaVA: Fairness-Aware Parameter-Efficient Fine-Tuning for Large Vision-Language Assistants","created":"2026-03-27","cited_by_count":1,"area":"mllm"},{"id":"2604.10755","title":"MMRareBench: A Rare-Disease Multimodal and Multi-Image Medical Benchmark","created":"2026-04-12","cited_by_count":1,"area":"mllm"},{"id":"2605.12650","title":"CRAFT: Clinical Reward-Aligned Finetuning for Medical Image Synthesis","created":"2026-05-12","cited_by_count":1,"area":"mllm"},{"id":"2605.17436","title":"Medical Context Distorts Decisions in Clinical Vision Language Models","created":"2026-05-17","cited_by_count":1,"area":"mllm"},{"id":"2605.20772","title":"VIHD: Visual Intervention-based Hallucination Detection for Medical Visual Question Answering","created":"2026-05-20","cited_by_count":1,"area":"mllm"},{"id":"2606.18609","title":"Hallucination Detection and Correction in Medical VLMs via Counter-Evidence Verification","created":"2026-06-17","cited_by_count":1,"area":"mllm"},{"id":"2606.21020","title":"CheXpercept: A Benchmark for Evaluating Expert-Level Lesion Perception in Chest X-rays","created":"2026-06-19","cited_by_count":1,"area":"mllm"},{"id":"2606.26079","title":"Same Evidence, Different Answer: Auditing Order Sensitivity in Multimodal Large Language Models","created":"2026-06-24","cited_by_count":1,"area":"mllm"},{"id":"2607.18577","title":"Attention Without Grounding: Causal Evaluation of Visual Explanations in Medical VLMs","created":"2026-07-20","cited_by_count":1,"area":"mllm"},{"id":"2609.30928","title":"UltraG-Bench: A Multi-task Benchmark for assessing Large Vision-Language Models on Pixel-level Evidence Grounding in Ultrasound","created":"2026-09-25","cited_by_count":1,"area":"mllm"},{"id":"2508.02525","title":"Accurate and Interpretable Postmenstrual Age Prediction via Multimodal Large Language Model","created":"2025-08-04","cited_by_count":0,"area":"mllm"},{"id":"2508.20345","title":"MedFoundationHub: A Lightweight and Secure Toolkit for Deploying Medical Vision Language Foundation Models","created":"2025-08-28","cited_by_count":0,"area":"mllm"},{"id":"2509.22261","title":"InfiMed-Foundation: Pioneering Advanced Multimodal Medical Models with Compute-Efficient Pre-Training and Multi-Stage Fine-Tuning","created":"2025-09-26","cited_by_count":0,"area":"mllm"},{"id":"2512.02566","title":"From Panel to Pixel: Zoom-In Vision-Language Pretraining from Biomedical Scientific Literature","created":"2025-12-02","cited_by_count":0,"area":"mllm"},{"id":"2512.21414","title":"A Tool Bottleneck Framework for Clinically-Informed and Interpretable Medical Image Understanding","created":"2025-12-24","cited_by_count":0,"area":"mllm"},{"id":"2512.22278","title":"FETAL-GAUGE: A Benchmark for Assessing Vision-Language Models in Fetal Ultrasound","created":"2025-12-25","cited_by_count":0,"area":"mllm"},{"id":"2601.14774","title":"Does medical specialization of VLMs enhance discriminative power?: A comprehensive investigation through feature distribution analysis","created":"2026-01-21","cited_by_count":0,"area":"mllm"},{"id":"2603.00426","title":"LLM-Bootstrapped Targeted Finding Guidance for Factual MLLM-based Medical Report Generation","created":"2026-02-28","cited_by_count":0,"area":"mllm"},{"id":"2603.00842","title":"MedGPT-oss: Training a General-Purpose Vision-Language Model for Biomedicine","created":"2026-03-01","cited_by_count":0,"area":"mllm"},{"id":"2603.07769","title":"MedQ-Deg: A Multidimensional Benchmark for Evaluating MLLMs Across Medical Image Quality Degradations","created":"2026-03-08","cited_by_count":0,"area":"mllm"},{"id":"2603.08921","title":"Vision-Language Models Encode Clinical Guidelines for Concept-Based Medical Reasoning","created":"2026-03-09","cited_by_count":0,"area":"mllm"},{"id":"2603.14579","title":"Medical Image Spatial Grounding with Semantic Sampling","created":"2026-03-15","cited_by_count":0,"area":"mllm"},{"id":"2603.16113","title":"PathGLS: Evaluating Pathology Vision-Language Models without Ground Truth through Multi-Dimensional Consistency","created":"2026-03-17","cited_by_count":0,"area":"mllm"},{"id":"2603.17246","title":"On the Cone Effect and Modality Gap in Medical Vision-Language Embeddings","created":"2026-03-18","cited_by_count":0,"area":"mllm"},{"id":"2603.18465","title":"MedQ-UNI: Toward Unified Medical Image Quality Assessment and Restoration via Vision-Language Modeling","created":"2026-03-19","cited_by_count":0,"area":"mllm"},{"id":"2603.18545","title":"CoDA: Exploring Chain-of-Distribution Attacks and Post-Hoc Token-Space Repair for Medical Vision-Language Models","created":"2026-03-19","cited_by_count":0,"area":"mllm"},{"id":"2603.19274","title":"CURE: A Multimodal Benchmark for Clinical Understanding and Retrieval Evaluation","created":"2026-02-28","cited_by_count":0,"area":"mllm"},{"id":"2603.19482","title":"Instruction-Free Tuning of Large Vision Language Models for Medical Instruction Following","created":"2026-03-19","cited_by_count":0,"area":"mllm"},{"id":"2603.19863","title":"MedQ-Engine: A Closed-Loop Data Engine for Evolving MLLMs in Medical Image Quality Assessment","created":"2026-03-20","cited_by_count":0,"area":"mllm"},{"id":"2603.21693","title":"Deterministic Hallucination Detection in Medical VQA via Confidence-Evidence Bayesian Gain","created":"2026-03-23","cited_by_count":0,"area":"mllm"},{"id":"2603.27176","title":"MEDIC-AD: Towards Medical Vision-Language Model's Clinical Intelligence","created":"2026-03-28","cited_by_count":0,"area":"mllm"},{"id":"2603.27737","title":"Synergizing Discriminative Exemplars and Self-Refined Experience for MLLM-based In-Context Learning in Medical Diagnosis","created":"2026-03-29","cited_by_count":0,"area":"mllm"},{"id":"2603.29492","title":"Calibrated Confidence Expression for Radiology Report Generation","created":"2026-03-31","cited_by_count":0,"area":"mllm"},{"id":"2604.09841","title":"Is There Knowledge Left to Extract? Evidence of Fragility in Medically Fine-Tuned Vision-Language Models","created":"2026-04-10","cited_by_count":0,"area":"mllm"},{"id":"2604.11868","title":"MedConcept: Unsupervised Concept Discovery for Interpretability in Medical VLMs","created":"2026-04-13","cited_by_count":0,"area":"mllm"},{"id":"2604.14363","title":"The Cost of Language: Centroid Erasure Exposes and Exploits Modal Competition in Multimodal Language Models","created":"2026-04-15","cited_by_count":0,"area":"mllm"},{"id":"2605.05594","title":"The Cost of Context: Mitigating Textual Bias in Multimodal Retrieval-Augmented Generation","created":"2026-05-07","cited_by_count":0,"area":"mllm"},{"id":"2605.05810","title":"CXR-ContraBench: Benchmarking Negated-Option Attraction in Medical VLMs","created":"2026-05-07","cited_by_count":0,"area":"mllm"},{"id":"2605.20495","title":"A Human-in-the-Loop Framework for Efficient Prompt Selection in Microscopy Vision-Language Models","created":"2026-05-19","cited_by_count":0,"area":"mllm"},{"id":"2605.22080","title":"JMed48k: A Multi-Profession Japanese Medical Licensing Benchmark for Vision-Language Model Evaluation","created":"2026-05-21","cited_by_count":0,"area":"mllm"},{"id":"2606.00123","title":"CardioLens: Revealing the Clinical Reality Gap of MLLMs via Multi-Sequence Cardiac MRI Evaluations","created":"2026-05-28","cited_by_count":0,"area":"mllm"},{"id":"2606.06379","title":"EasyLens: A Training-Free Plug-and-Play Subtle-Lesion Representation Amplifier for Medical Vision-Language Models","created":"2026-06-04","cited_by_count":0,"area":"mllm"},{"id":"2606.10066","title":"A Controlled Audit of Pretraining Contamination in Public Medical Vision-Language Benchmarks","created":"2026-06-08","cited_by_count":0,"area":"mllm"},{"id":"2606.12590","title":"Analyzing and Improving Fine-grained Preference Optimization in Medical LVLMs","created":"2026-06-10","cited_by_count":0,"area":"mllm"},{"id":"2606.20723","title":"Evaluation of Medical Vision Language Models HuluMed and MedGemma, and general purpose chatbots Gemma 3, ChatGPT Plus, and Claude Pro on real previously unseen wound images","created":"2026-06-16","cited_by_count":0,"area":"mllm"},{"id":"2606.21194","title":"MEDLAYXPLAIN: Benchmarking the Expert-Lay Gap in Medical Vision-Language Models","created":"2026-06-19","cited_by_count":0,"area":"mllm"},{"id":"2606.28520","title":"Detecting Clinical Hallucinations in LVLMs via Counterfactual Visual Grounding Uncertainty","created":"2026-06-26","cited_by_count":0,"area":"mllm"},{"id":"2606.29232","title":"When Does Synthetic CT Transfer? A Label-Free Donor/Host Diagnostic for Medical Vision-Language Model Routing on Real Lung CT","created":"2026-06-28","cited_by_count":0,"area":"mllm"},{"id":"2606.30201","title":"SHOVIR: A Benchmark for Evaluating Vision Shortcut Learning in Radiology Report Generation","created":"2026-06-29","cited_by_count":0,"area":"mllm"},{"id":"2607.04344","title":"IRIS: An Intelligent Vision-Language System for Ocular Surface Diseases via Topic Tree and Scene-Driven VQA Generation","created":"2026-07-05","cited_by_count":0,"area":"mllm"},{"id":"2607.18988","title":"DobicVLM: Aligning Chest X-Ray Report Generation with Clinically-Grounded Programmatic Rewards via Group Relative Policy Optimization","created":"2026-07-21","cited_by_count":0,"area":"mllm"},{"id":"2608.05341","title":"Positive-Unlabeled Preference Optimization For Chest X-ray Report Generation","created":"2026-08-05","cited_by_count":0,"area":"mllm"},{"id":"2608.07550","title":"Auditing Medical Vision-Language Models on Chest Radiographs: Estimating Reference Agreement Across Institutions","created":"2026-08-01","cited_by_count":0,"area":"mllm"},{"id":"2608.10635","title":"MedUP: Awakening Unified Understanding and Perception in Medical Vision-Language Models","created":"2026-08-11","cited_by_count":0,"area":"mllm"},{"id":"2608.11534","title":"CT-$\\Delta$Bench: A Benchmark for Longitudinal 3D Medical Imaging Difference Reporting with Vision-Language Models","created":"2026-08-12","cited_by_count":0,"area":"mllm"},{"id":"2608.20549","title":"Volumetric Radiology AI in the Era of Multimodal Large Language Models","created":"2026-08-20","cited_by_count":0,"area":"mllm"},{"id":"2609.21763","title":"Beyond Benchmark Scores: Auditing Medical Vision-Language Models for Chest X-Ray Tuberculosis Screening","created":"2026-09-18","cited_by_count":0,"area":"mllm"},{"id":"2609.32352","title":"EyeVQA: Benchmarking Ophthalmic Vision-Language Models from Recognition to Spatial Grounding","created":"2026-09-26","cited_by_count":0,"area":"mllm"},{"id":"2609.33158","title":"FOCUS: Benchmarking Retinal Model Generalization from Foundation Vision Encoders to Multimodal LLMs","created":"2026-09-27","cited_by_count":0,"area":"mllm"},{"id":"2609.40361","title":"Ranking-Aware Prompt Optimization for Multimodal Clinical Diagnosis","created":"2026-09-30","cited_by_count":0,"area":"mllm"},{"id":"2610.02270","title":"Reliability Stress Tests and Decision-Time Routing for Chest X-ray Vision-Language Models","created":"2026-10-01","cited_by_count":0,"area":"mllm"},{"id":"2610.07339","title":"A doctrine-grounded visual question answering dataset for Tactical Combat Casualty Care","created":"2026-10-05","cited_by_count":0,"area":"mllm"},{"id":"2610.10238","title":"Geometry-Supervised Visual Representation Learning for Multi-Phenotype Lesion Interpretation in Medical VLMs","created":"2026-10-07","cited_by_count":0,"area":"mllm"},{"id":"2610.11312","title":"MedBenchAgent: Towards Systematic Automation of Medical VLM Benchmark Construction","created":"2026-10-08","cited_by_count":0,"area":"mllm"},{"id":"2610.13510","title":"Retrieval-Augmented Multimodal Language Model with Heterogeneous Evidence Integration for Clinical Prognosis","created":"2026-10-09","cited_by_count":0,"area":"mllm"}],"next_cursor":"100"}