I-Viewer: An Online Digital Pathology Analysis Platform with Agentic-RAG AI Copilot

Digital pathology has seen significant advancements in artificial intelligence (AI) applications. However, challenges persist in integrating these solutions into digital pathology platforms for human and AI collaborations. We introduce I-Viewer, an online AI Copilot framework designed to facilitate real-time human-AI and human-human collaboration for digital pathology analysis. The I-Viewer platform enables precise annotations and descriptions from tissue to the nuclei level through an Agentic-Retrieval Augmented Generation (RAG) system. By leveraging agents' outputs as reference points, aggregating information through the RAG system, and incorporating Large Language Models (LLM) for human feedback and refinement, I-Viewer sets a new standard for collaborative and accurate digital pathology analysis. We demonstrate I-Viewer's effectiveness on different pathology tasks using three datasets across different types of cancers, including non-small cell lung cancer, breast cancer, and colorectal cancer. The results show that I-Viewer achieves significant improvements in annotation speed and accuracy for pathology tasks, such as detecting cell morphology, cellular structures, and tumor growth patterns, outperforming current individual foundation models. Through its advanced AI agents, collaborative features, and LLM integrations, I-Viewer optimizes diagnostic workflows in clinical care and biomedical research.