The field of medical image analysis is rapidly advancing with the development of vision-language models (VLMs) that can integrate visual and textual information to improve diagnostic accuracy. Recent research has focused on applying VLMs to specialized domains such as dermatology, gastroenterology, and colonoscopy, where subtle regional findings and specialized clinical knowledge are crucial for accurate diagnosis. The use of large vision-language models, such as MedDAM and SkinVL, has shown promising results in generating region-specific descriptions and detecting skin diseases. Additionally, the introduction of new datasets, such as MM-Skin and Gut-VLM, has facilitated the development of more accurate and robust VLMs. Noteworthy papers in this area include MedDAM, which proposes a comprehensive framework for region-specific captioning in medical images, and MM-Skin, which introduces a large-scale multimodal dermatology dataset. Other notable papers include Hallucination-Aware Multimodal Benchmark for Gastrointestinal Image Analysis, which addresses the issue of hallucination in VLMs, and MAKE, which introduces a multi-aspect knowledge-enhanced vision-language pretraining framework for zero-shot dermatological tasks.