Medical-Checklist: Assessing the Comprehension of Medical Images by Multimodal Models.

Generating Image-Based Multiple-Choice Questions with Multimodal Large Language Models: Expert and Psychometric Evaluation.

Do Accuracy Gains Reflect Genuine Visual Understanding? A Multi-Model Evaluation of Vision-Language Models in Thoracic Imaging.

RadSight: Towards Perceptually Reliable Multimodal Radiology Image Understanding

RadSight is proposed, a perception-driven MLLM built upon a dual 2D/3D encoder architecture that preserves native imaging spatial structures that achieves consistent improvements on public 2D and 3D medical benchmarks, further demonstrating that robust low-level visual perception is a critical foundation for reliable clinical understanding.

Entity-Aware Medical Image Captioning

In order to produce meaningful textual interpretations of intricate clinical images, medical image captioning has become a significant field of study at the nexus of computer vision and natural language processing. Due to the lack of explicit modeling of medical entities, current methods frequently fail to produce descriptions that are both semantically valid and clinically useful, despite notable advances in deep learning and vision language modeling. Typical captioning methods in particular, fall short of being able to retrieve fine grained diagnostic information and maintain semantic consistency with clinical findings as they focus on global features. This paper addresses these limitations by presenting an entity-aware medical image captioning approach which aims to identify and incorporate clinically relevant entities into the caption generation, including but not limited to, diagnostic finding, anatomical structures, or diagnostic characteristics. The proposed method utilizes entity level representations as a means of guiding the captioning process thereby ensuring a tighter semantic consistency between visual modalities and the resultant textual output. Consequently, this leads to more comprehensible, informative and clinically relevant generated reports. Additionally, inclusion of entity awareness can aid the model in effectively understanding the relationships between medical concepts leading to captions more consistent with medical expertise. The results demonstrate that explicit modeling with structured semantic information within vision-language frameworks are crucial and that entity-aware methods have the potential to greatly improve captioning. This work has the ability to help advance health intelligence applications which will serve to better assist clinical decision making, scale the processing of medical images, and facilitate accurate medical documentation.

Radiological VQA with Multimodal LLMs: Performance and Insights

The exponential growth in medical imaging volumes necessitates scalable, reliable diagnostic support systems capable of augmenting clinical workflows. This article presents a systematic quantitative evaluation of state-of-the-art Multimodal Large Language Models (MLLMs) for radiology Visual Question Answering (VQA), a task requiring integrated visual perception and clinical reasoning. We benchmark five leading models — GPT5-Nano, Gemini 3 Flash, Qwen3-VL-8B, LLaVA Next, and Llama 3.2 Vision — on the VQA-RAD dataset under a rigorous zero-shot protocol with standardized prompts and comprehensive precision–recall–F1 evaluation. Our empirical analysis reveals that Gemini 3 Flash achieves superior balanced performance (F1 = 0.78, Accuracy = 0.78, Recall = 0.83), while Qwen3-VL-8B attains the highest precision (0.78) while also maintaining competitive recall. These outcomes demonstrate that general-purpose MLLMs can perform competitively with specialized medical models in tasks such as modality and organ recognition, but still struggle with abnormality detection and complex clinical reasoning. The findings reinforce that MLLMs currently serve best as assistive decisionsupport tools rather than autonomous diagnostic agents, and highlight the potential of retrieval-augmented and context-aware strategies for improving clinical reliability and interpretability.

Large Language Models for Differential Diagnosis: A Survey of Performance, Collaboration, and Technical Strategies

Current evidence supports clinician-supervised use of artificial intelligence (AI) systems rather than autonomous diagnosis, pending prospective and specialty-specific evaluation.