ARTIFICIAL INTELLIGENCE IN BIPOLAR DISORDER MANAGEMENT: ENHANCING DIAGNOSIS, MONITORING, AND PREDICTION

Resumo

This narrative review delves into the potential of artificial intelligence (AI) in managing bipolar disorder (BD). A comprehensive literature search was conducted across multiple databases, including Scopus, Web of Science, PubMed, IEEE Xplore, ScienceDirect, Directory of Open Access Journals (DOAJ), and JSTOR, focusing on articles published between January 2010 and December 2022. The review identifies promising AI techniques, particularly machine learning (ML) and artificial neural networks (ANN), that enhance diagnostic accuracy and continuously monitor and predict clinical outcomes for BD. AI methods have demonstrated significant potential in differentiating BD from other psychiatric conditions, such as major depressive disorder (MDD) and schizophrenia, with reported accuracies ranging from 49.5% to 96.2%. Moreover, AI-driven systems utilizing smartphones and wearable devices have shown high accuracy in monitoring mood states and predicting mood episode recurrences. Predictive models using ML algorithms have also been effective in forecasting depressive relapses and identifying cognitive dysfunctions in the early stages of BD. The review underscores the transformative potential of AI in BD management, particularly in predicting clinical outcomes, and calls for further research to overcome existing limitations.