Abstract: Abstract: Accurate diagnosis of pigmented skin lesions presents a complex and challenging task. In contemporary medical practice， intelligent diagnostic tools can significantly enhance the precision of both diagnosis and treatment. This study proposes an innovative multimodal large language model， SkinCPM-V， to address diagnostic challenges associated with textural patterns， hair artifacts， and vascular structures in dermoscopic images. SkinCPM-V is deeply optimized based on MiniCPM-V， and specially customized for the characteristics of skin lesions. It has been extensively trained on publicly available dermatological datasets from Kaggle， leveraging the LoRA technique to achieve efficient parameter fine-tuning. Comprehensive evaluations reveal that SkinCPM-V achieves exceptional performance， with BLEU-4， ROUGE-1， ROUGE-2， and ROUGE-L scores of 0.8880， 0.9380， 0.9104， and 0.9349， respectively， indicating a high level of alignment between generated outputs and reference standards. Additionally， the model’s effectiveness in real-world diagnostic tasks is validated through F1 score of 0.9067， precision of 0.9028， and recall of 0.9444， highlighting its robust performance. Compared to other multimodal large language models， SkinCPM-V demonstrates superior results across all evaluation metrics. This highlights its ability to generate high-quality textual descriptions and underscores its potential for integration into clinical workflows. The findings of this study validate the utility of SkinCPM-V in the diagnosis of pigmented skin lesions and pave the way for broader applications of multimodal large language models in medical domains， offering a promising avenue for advancing diagnostic technologies.

Key words: Key words: pigmented skin lesions； automated diagnosis； multimodal large language model； dermoscopic diagnosis； parameter-efficient fine-tuning； model evaluation ,