Mind the Rarities: Can Rare Skin Diseases Be Reliably Diagnosed via Diagnostic Reasoning?

Large vision-language models (LVLMs) demonstrate strong performance in dermatology; however, evaluating diagnostic reasoning for rare conditions remains largely unexplored. Existing benchmarks focus on common diseases and assess only final accuracy, overlooking the clinical reasoning process, which is critical for complex cases. We address this gap by constructing DermCase, a long-context benchmark derived from peer-reviewed case reports. Our dataset contains 26,030 multi-modal image-text pairs and 6,354 clinically challenging cases, each annotated with comprehensive clinical information and step-by-step reasoning chains. To enable reliable evaluation, we establish DermLIP-based similarity metrics that achieve stronger alignment with dermatologists for assessing differential diagnosis quality. Benchmarking 22 leading LVLMs exposes significant deficiencies across diagnosis accuracy, differential diagnosis, and clinical reasoning. Fine-tuning experiments demonstrate that instruction tuning substantially improves performance while Direct Preference Optimization (DPO) yields minimal gains. Systematic error analysis further reveals critical limitations in current models' reasoning capabilities.

ClinKD: Cross-Modal Clinic Knowledge Distiller For Multi-Task Medical Images

Med-VQA (Medical Visual Question Answering) is a crucial subtask within the broader VQA (Visual Question Answering) domain. This task requires a visual question answering system to analyze the provided image and corresponding question,offering reasonable analysis and suggestions to assist medical professionals in making pathological diagnoses, or ideally, enabling the system to independently provide correct diagnoses. Furthermore, more advanced Med-VQA tasks involve Referring and Grounding, which not only require the system to accurately comprehend medical images but also to pinpoint specific biological locations within those images. While many large pre-trained models have demonstrated substantial VQA capabilities,challenges persist in the medical imaging domain. The intricacy of biological features in medical images and the scarcity of high-quality medical image datasets, combined with the fact that current models are not tailored for the medical field in terms of architecture and training paradigms, hinder the full exploitation of model generalization. This results in issues such as hallucination in Visual Grounding. In this paper, we introduce the ClinKD model, which incorporates modifications to model position encoding and a diversified training process. Initially, we enhance the model's ability to perceive image and modality variations by using Med-CLIP Guided Rotary Position Embedding. Subsequently, we leverage distillation to provide prior knowledge to the model before using complete training data. Additionally, the feedback-based training process during the formal training phase further enhances data utilization. Notably, under unchanged evaluation protocols, we achieve a new state-of-the-art performance on the Med-GRIT-270k dataset, and the Med-CLIP Guided Rotary Position Embedding approach presents potential for generalizing to universal model position encoding.