Recent advances in large language models (LLMs) have demonstrated impressive medical reasoning capabilities. However, current evaluation methods are mostly limited to static case vignettes and multiple-choice questions which fail to reflect the complexity, uncertainty, and iterative nature of real-world clinical decision-making. To bridge this gap, we propose **DiagBench**, a novel benchmark where models interact dynamically with a LLM based Patient Simulator, querying relevant clinical details to formulate accurate diagnoses. To complement this, we introduce **MedConvBench**, a diagnostic conversation benchmark designed to assess the relevance and quality of model-generated clinical reasoning. To further address the interpretability and alignment challenges of AI-assisted diagnosis, we develop a modular and medically grounded framework called **VAIDYA** that mirrors a physician’s stepwise diagnostic reasoning. This structured approach improves transparency and yields substantial performance gains over base LLMs. Our work takes a critical step toward aligning AI systems with real-world clinical practices by combining dynamic interaction, interpretability, and clinical validation.