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. 1

At first, this text has analyzed the epistemology reason lacked in study of ethics to new-type commercial mode of e-commerce; Then, on the basis of the visual angle of thinking of the system, it has divided the numerous and disorderly and complicated Chinese e-commerce ethics problem into four kind and put forward the corresponding ethics norm and cultural countermeasure of China's traditional ethics.