Imagine an AI that could instantly pinpoint the exact location of a medical anomaly within an image, simply from a short textual description. This isn't science fiction, it's the groundbreaking potential of medical visual grounding. Researchers are tackling this complex challenge, pushing the boundaries of AI's ability to interpret and analyze medical images. A key hurdle lies in the sheer cost and data requirements for training effective medical Multimodal Large Language Models (MLLMs). These advanced models combine the linguistic prowess of LLMs with the ability to process visual information, unlocking a new era of medical image understanding. However, training them from scratch demands enormous resources. This is where a new approach, Parameter-efficient Fine-tuning medical multimodal large language models for Medical Visual Grounding (PFMVG), comes in. Instead of building a new model from the ground up, PFMVG cleverly leverages the existing power of a pre-trained MLLM, MiniGPT-v2. This technique, known as Parameter-Efficient Fine-Tuning (PEFT), significantly reduces the computational burden and the need for vast medical datasets. PFMVG employs a two-stage fine-tuning process. First, it's trained on image captioning tasks to build a strong foundation of medical knowledge, connecting images with their textual descriptions. Then, it's refined on a specific medical visual grounding dataset, MS-CXR, learning to precisely link short textual descriptions to the corresponding regions of interest within medical images. The results are impressive. PFMVG outperforms existing methods and even significantly surpasses the performance of GPT-4v on the MS-CXR dataset, demonstrating its superior accuracy in locating diseases like pneumothorax within chest X-rays. While these initial findings are promising, challenges remain. Further research is needed to enhance the model's understanding of complex medical terminology and its ability to generalize across diverse medical image types. However, PFMVG represents a significant step forward, offering a more efficient and effective way to harness the power of AI for enhanced medical image analysis. This innovative approach has the potential to revolutionize medical diagnosis and treatment, paving the way for more precise and timely healthcare interventions.