Artificial intelligence for patent ductus arteriosus-a systematic review.

Introduction: Optimal management of patent ductus arteriosus (PDA) remains controversial. Complexity in severity appraisal, high-dimensional data, and the need for longitudinal, individualized assessment make PDA a compelling candidate for Artificial Intelligence (AI)-driven approaches. This systematic review evaluates AI research in the context of PDA, identifying strengths, limitations, and future directions. Methods: Following PRISMA 2020, databases were searched for peer-reviewed articles from January 1, 2010, to May 31, 2025. Eleven studies met inclusion criteria. Data on design, population, sources, AI methods, performance, validation, limitations, and explainability were extracted. Risk of bias was assessed using the Prediction model Risk of Bias Assessment Tool and Joanna Briggs Institute Critical Appraisal Checklist; reporting quality using the Minimum Information about Clinical AI Modeling checklist. Heterogeneity precluded meta-analysis; therefore findings were synthesized narratively. Results: Eleven studies addressed diagnosis/screening ( Conclusions: Artificial intelligence shows feasibility for supporting PDA risk stratification, diagnosis, severity assessment, and prediction of treatment-related outcomes. However, current applications remain in early, pilot-stage development and are not yet suitable for clinical implementation. Future work should prioritize clinically meaningful tasks, scientifically rigorous and bias-aware methodologies, larger and more representative cohorts, and systematic external validation. Fairness, explainability, and reproducibility must be addressed to support translation. Continued methodological refinement and clinical grounding will be key to unlocking the potential of these technologies for this highly vulnerable patient population in the future.