PhenoPatient: Clinically Plausible and Diverse De Novo Virtual Patients Aligned with Real-World Phenotype Distributions

Interactive virtual patients support medical-AI training and evaluation, but many existing simulators depend on real clinical records. Limited shareability, privacy and data-use restrictions, and scarce rare-disease records constrain their coverage. De novo simulation removes one-to-one record dependence but creates a central tension between individual coherence and cohort diversity: maximizing typicality can produce stereotyped cases, whereas expanding coverage can introduce within-patient contradictions. We introduce PhenoPatient, which reconciles these objectives while targeting real-world disease-phenotype distributions. A medical literature retrieval engine supports a severity-conditioned phenotype atlas; probabilistic sampling, cross-modal correction, and an immutable, rule-checked ledger produce diverse, coherent patients. Evaluation spans individual-level plausibility, population-level plausibility, and within-disease diversity. Across 1,000 completed encounters per implementation and 50 diseases, PhenoPatient achieves individual-level plausibility comparable to record-grounded simulators, with higher observed scores on most population-level measures and all five within-disease coverage and diversity measures. Under fixed interaction protocols, it yields the largest observed GPT-5.6 Sol–Qwen3.8-27B gaps in broad-category and specific-disease diagnosis, consistent with heterogeneous cases exposing doctor-model differences. Replacing GPT-5.6 Sol with Qwen3.8-27B for patient dialogue produces the smallest mean quality decline among evaluated simulators, consistent with ledger-supported smaller-model operation. Blinded physicians assign the highest reported mean plausibility, logical-consistency, and patient-likeness ratings. These findings support scalable, disease-diverse cases for model evaluation, training-data construction, and controlled clinical-reasoning training.