SkinGPT-X: A Self-Evolving Collaborative Multi-Agent System for Transparent and Trustworthy Dermatological Diagnosis

While recent advancements in Large Language Models have significantly advanced dermatological diagnosis, monolithic LLMs frequently struggle with fine-grained, large-scale multi-class diagnostic tasks and rare skin disease diagnosis owing to training data sparsity, while also lacking the interpretability and traceability essential for clinical reasoning. Although multi-agent systems can offer more transparent and explainable diagnostics, existing frameworks are primarily concentrated on Visual Question Answering and conversational tasks, and their heavy reliance on static knowledge bases restricts adaptability in complex real-world clinical settings. Here, we present SkinGPT-X, a multimodal collaborative multi-agent system for dermatological diagnosis integrated with a self-evolving dermatological memory mechanism. By simulating the diagnostic workflow of dermatologists and enabling continuous memory evolution, SkinGPT-X delivers transparent and trustworthy diagnostics for the management of complex and rare dermatological cases. To validate the robustness of SkinGPT-X, we design a three-tier comparative experiment. First, we benchmark SkinGPT-X against four state-of-the-art LLMs across four public datasets, demonstrating its state-of-the-art performance with a +9.6% accuracy improvement on DDI31 and +13% weighted F1 gain on Dermnet over the state-of-the-art model. Second, we construct a large-scale multi-class dataset covering 498 distinct dermatological categories to evaluate its fine-grained classification capabilities. Finally, we curate the rare skin disease dataset, the first benchmark to address the scarcity of clinical rare skin diseases which contains 564 clinical samples with eight rare dermatological diseases. On this dataset, SkinGPT-X achieves a +9.8% accuracy improvement, a +7.1% weighted F1 improvement, a +10% Cohen's Kappa improvement.

BioinfoMCP: A Unified Platform Enabling MCP Interfaces in Agentic Bioinformatics

Bioinformatics tools are essential for complex computational biology tasks, yet their integration with emerging AI-agent frameworks is hindered by incompatible interfaces, heterogeneous input-output formats, and inconsistent parameter conventions. The Model Context Protocol (MCP) provides a standardized framework for tool-AI communication, but manually converting hundreds of existing and rapidly growing specialized bioinformatics tools into MCP-compliant servers is labor-intensive and unsustainable. Here, we present BioinfoMCP, a unified platform comprising two components: BioinfoMCP Converter, which automatically generates robust MCP servers from tool documentation using large language models, and BioinfoMCP Benchmark, which systematically validates the reliability and versatility of converted tools across diverse computational tasks. We present a platform of 38 MCP-converted bioinformatics tools, extensively validated to show that 94.7% successfully executed complex workflows across three widely used AI-agent platforms. By removing technical barriers to AI automation, BioinfoMCP enables natural-language interaction with sophisticated bioinformatics analyses without requiring extensive programming expertise, offering a scalable path to intelligent, interoperable computational biology.