TITLE:
Cross-Institutional Generalization of Psychiatric AI Models: A Domain Adaptation and Robustness Framework for Multi-Site Clinical Deployment
AUTHORS:
Rocco de Filippis, Abdullah Al Foysal
KEYWORDS:
Domain Adaptation, Transfer Learning, Psychiatric Ai, Cross-Institutional Generalization, Federated Learning, Machine Learning Robustness, Precision Psychiatry, Clinical Decision Support
JOURNAL NAME:
Open Access Library Journal,
Vol.13 No.5,
May
27,
2026
ABSTRACT: The deployment of artificial intelligence models in psychiatric care faces a critical challenge: models trained in one healthcare institution often fail to generalize across different populations, clinical practices, and healthcare systems. This study investigates domain adaptation and robustness techniques to ensure psychiatric AI models maintain predictive accuracy when deployed across diverse institutional settings. We developed a comprehensive framework integrating transfer learning, adversarial domain adaptation, and federated learning approaches to address cross-institutional heterogeneity. Using synthetic clinical datasets representing six distinct hospitals with varying patient demographics, treatment protocols, and documentation practices, we evaluated model performance under realistic non-independent and identically distributed conditions. Our domain adaptation approach achieved mean AUC-ROC of 0.847 (95% CI: 0.815 - 0.879) across target institutions, representing a 9.1% improvement over direct model deployment without adaptation. Feature importance analysis identified age, depression severity scores, prior hospitalizations, and medication adherence as domain-invariant predictors, while hospital type and documentation style emerged as domain-specific confounders. Risk stratification analysis demonstrated consistent performance across institutions, with observed readmission rates ranging from 7.5% - 8.9% in the lowest risk category to 79.5% - 83.8% in the highest risk category. These findings establish that domain adaptation techniques can effectively mitigate institutional heterogeneity, enabling reliable deployment of psychiatric AI models across diverse healthcare settings while maintaining predictive accuracy and clinical utility.