Medical diagnostics is a high-stakes and complex domain that is critical to patient care. However, current evaluations of large language models (LLMs) remain limited in capturing key challenges of clinical diagnostic scenarios. Most rely on benchmarks derived from public exams, raising contamination bias that can inflate performance, and they overlook the confounded nature of real consultations beyond textbook cases. Recent dynamic evaluations offer a promising alternative, but often remain insufficient for diagnosis-oriented benchmarking, with limited coverage of clinically grounded confounders and trustworthiness beyond accuracy. To address these gaps, we propose DyReMe, a dynamic benchmark for medical diagnostics that provides a controlled and scalable stress test of diagnostic robustness. Unlike static exam-style questions, DyReMe generates fresh, consultation-style cases that incorporate clinically grounded confounders, such as differential diagnoses and common misdiagnosis factors. It also varies expression styles to capture heterogeneous patient-style descriptions. Beyond accuracy, DyReMe evaluates LLMs on three additional clinically relevant dimensions: veracity, helpfulness, and consistency. Our experiments show that this dynamic approach yields more challenging assessments and exposes substantial weaknesses of stateof-the-art LLMs under clinically confounded diagnostic settings. These findings highlight the urgent need for evaluation frameworks that better assess trustworthy medical diagnostics under clinically grounded confounders.

Medical information retrieval (MIR) is vital for accessing knowledge from electronic health records, scientific literature, and medical databases, supporting applications such as medical education, patient queries, and clinical diagnosis. However, effective zero-shot dense retrieval in the medical domain remains difficult due to the scarcity of relevance-labeled data. To address this challenge, we propose **S**elf-**L**earning **Hy**pothetical **D**ocument **E**mbeddings (**SL-HyDE**), a framework that leverages large language models (LLMs) to generate hypothetical documents conditioned on a query. These documents encapsulate essential medical context, guiding dense retrievers toward the most relevant results. SL-HyDE further employs a self-learning mechanism that iteratively improves pseudo-document generation and retrieval using unlabeled corpora, eliminating the need for labeled data. In addition, we introduce the Chinese Medical Information Retrieval Benchmark (CMIRB), a comprehensive evaluation suite reflecting real-world medical scenarios, comprising five tasks and ten datasets. By benchmarking ten models on CMIRB, we provide a rigorous standard for evaluating MIR systems. Experimental results demonstrate that SL-HyDE significantly outperforms HyDE in retrieval accuracy, while exhibiting strong generalization and scalability across diverse LLM and retriever configurations. Our code and data are publicly available at: https://github.com/ll0ruc/AutoMIR.