Background/Objectives: Bipolar disorder (BD) is a complex and chronic mental health condition that poses significant challenges for both patients and healthcare providers. Traditional treatment methods, including medication and therapy, remain vital, but there is increasing interest in the application of artificial intelligence (AI) to enhance BD management. AI has the potential to improve mood episode prediction, personalize treatment plans, and provide real-time support, offering new opportunities for managing BD more effectively. Our primary objective was to explore the potential role of AI in transforming the management of BD, specifically in mood tracking, prediction, and personalized treatment regimens. Methods: To explore the potential role of AI in transforming BD management, we conducted a review of recent literature using key search terms. We included studies that discussed AI applications in mood tracking, prediction, and treatment personalization. The studies were selected based on their relevance to AI’s role in BD management, with attention to the PICO criteria: Population—individuals diagnosed with BD; Intervention—AI tools for mood prediction, treatment personalization, and real-time support; Comparison—traditional treatment methods (when available); Outcome—measures of mood episode prediction, treatment effectiveness, and improvements in patient care. Results: The findings from recent research reveal promising developments in the use of AI for BD management. Studies suggest that AI-powered tools can enable more proactive and personalized care, improving treatment outcomes and reducing the burden on healthcare professionals. AI’s ability to analyze data from wearable devices, smartphones, and even social media platforms provides valuable insights for early detection and more dynamic treatment adjustments. Conclusions: While AI’s application in BD management is still in its early stages, it presents transformative potential for improving patient care. However, further research and development are crucial to fully realize AI’s potential in supporting BD patients and optimizing treatment efficacy.

Background/Objectives: Bipolar disorder (BD) is a chronic, severe mental health condition characterized by episodes of mood instability, including manic and depressive episodes. While pharmacological interventions remain foundational in BD treatment, psychotherapy offers significant benefits by addressing the psychological and behavioral components that contribute to mood episodes and overall functioning. The primary objective of this short communication is to propose new directions in psychotherapy for treating bipolar disorder, focusing on integrative models that combine evidence-based therapies such as Cognitive Behavioral Therapy (CBT), Interpersonal and Social Rhythm Therapy (IPSRT), Family-Focused Therapy (FFT), and mindfulness-based approaches. By integrating these therapies, clinicians can target both cognitive distortions and emotional dysregulation while simultaneously stabilizing sleep–wake cycles and improving interpersonal functioning. The secondary objective emphasizes the importance of better understanding and psychoeducation in family therapy, which can promote a better understanding of BD among family members and ensure more effective management of the disorder in daily life. Methods: We explore the potential of Cognitive Behavioral Therapy (CBT), Interpersonal and Social Rhythm Therapy (IPSRT), Family-Focused Therapy (FFT), and mindfulness-based interventions in enhancing symptom management and preventing relapse. Results: We identified psychoeducation and family therapy as critical components in supporting patients and improving treatment adherence. These therapeutic interventions play a pivotal role in enhancing patient engagement, improving coping strategies, and facilitating better overall treatment outcomes. Conclusions: We propose a multidisciplinary approach, integrating psychotherapy with pharmacotherapy, to optimize long-term outcomes and improve the overall quality of life for individuals with bipolar disorder.

To start with identifying an emerging issue, the first part of the chapter will outline problems of abuse survivors related to trauma or those that appear to be unrelated in the first place but make effects of the traumatic experience harder to handle. Secondly, the chapter will explore barriers to mental health practitioners responding to domestic violence and abuse cases adequately. Thirdly, the focus will be on elaborating practices and principles that can apply in the mental health institutions to recognize the actual realities and needs of abuse survivors and prevent re-traumatization by using trauma-informed care. Finally, in the final part, the authors argue about whether it is reasonable to call for the shift from the medical view in reducing stigma around mental health problems to promote environmental and interpersonal explanations rather than biomedical. In line with the overall statistics on the dominant pattern of violence against women, this chapter will mainly focus on gender-specific mental health and abuse aspects. Request access from your librarian to read this chapter's full text.