We introduce Deep-DxSearch, an end-to-end agentic RAG system trained with reinforcement learning for traceable diagnostic reasoning. Our framework addresses knowledge gaps and hallucinations in medical LLMs by constructing a large-scale medical retrieval corpus and using the LLM as a core agent with tailored rewards. Deep-DxSearch consistently outperforms prompt-engineering and training-free RAG approaches, achieving substantial gains over GPT-4o, DeepSeek-R1, and other medical frameworks for both common and rare disease diagnosis.

We present M³Builder, an LLM-powered multi-agent system for autonomous end-to-end medical imaging AI model training—the first to automate ML in medical imaging, lowering the threshold for clinicians to develop and apply AI models, and promoting the widespread adoption of AI tools in real clinical scenarios.

We evaluate modern LLMs as agent cores in radiology environments through a comprehensive dataset, innovative evaluation platform, and performance assessments of leading models. Results highlight key challenges in tool comprehension, information synthesis, and format maintenance, confirming that while promising, current LLMs are not yet ready to serve as standalone radiology agent cores.

We build up an academically accessible, large-scale diagnostic dataset, present a knowledge enhanced model architecture that enables processing arbitrary number of input scans from various imaging modalities, and initialize a new benchmark for multi-modal multi-anatomy long-tailed diagnosis. Our method shows superior results and serves as a pre-trained model that can be finetuned to benefit diagnosis on various external datasets.

We present recent efforts on assessing GPT-4V for multimodal medical diagnosis through case studies, covering 17 human body systems across 8 clinical imaging modalities, including radiology and pathology.