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Real-world clinical care is a long-horizon decision-making process in which patient state and evidence evolve over multiple turns. In contrast, static, single-turn medical VQA is insufficient for training and evaluating interactive medical agents. To bridge this gap, we introduce DiagWorld, a scalable multimodal virtual medical environment based on large-scale real-world clinical interactions—an EHR-grounded simulator that supports multi-turn interviewing and tool-augmented evidence acquisition under realistic clinical workflows. Built from real patient records in MIMIC-IV, our pipeline transforms longitudinal EHR data—admissions, laboratory tests, imaging, and clinical notes—into 500K+ interactive trajectories, enabling large-scale training and evaluation. Unlike prior EHR-only simulators, we integrate external disease-level medical databases for consistency-aware completion and constraint validation, reducing diagnostic confounding from missing or noisy EHR signals and enabling scaling in patient volume and interaction horizon without amplifying artifacts. Leveraging this environment, we train a diagnostic agent with confidence-based process supervision: rather than sparse terminal rewards or manually annotated key steps, we provide intermediate rewards tied to increases in confidence toward the correct diagnosis, reinforcing informative questioning, retrieval, test ordering, and CXR analysis. This shaping improves sample efficiency and stabilizes learning in large-scale interactive training.