THE ROLE OF AI IN ENHANCING PSYCHOLOGICAL SCREENING FOR POSTPARTUM DEPRESSION-A NARRATIVE REVIEW

Abstract

Background: Postpartum depression (PPD) is a prevalent and often underdiagnosed mental health condition affecting up to one in five women globally after childbirth. Despite growing awareness, many cases remain untreated due to stigma, limited access to mental health services, and lack of timely screening. The emergence of artificial intelligence (AI) technologies offers new possibilities for enhancing early detection and personalized intervention through digital platforms and mobile applications.

Objective: This narrative review aims to explore recent advancements in AI-driven tools for screening postpartum depression, with an emphasis on their clinical relevance, technological frameworks, implementation challenges, and future directions.

Main Discussion Points: The review synthesizes evidence across eight recent studies, identifying key themes including the evolution of AI algorithms for symptom recognition, integration into mobile health (mHealth) platforms, comparison with traditional screening tools, and the potential of AI to increase access in underserved populations. Challenges such as methodological biases, ethical concerns, data privacy, and limited generalizability are critically examined. Variability in screening instruments and the lack of standardized clinical pathways further complicate the application of AI in routine care.

Conclusion: AI-based tools hold significant promise in transforming PPD screening by offering scalable, user-friendly, and proactive approaches to early detection. However, the current evidence base is limited and calls for rigorous, large-scale, and culturally sensitive research. Future studies must address ethical integration, validation standards, and real-world effectiveness to ensure clinical reliability and equity.