Advances in data science are reshaping diagnostic and therapeutic models in mental healthcare. Artificial intelligence and machine learning in psychiatry are enabling earlier detection of mental illness through predictive analytics, pattern recognition in speech or behavior, and interpretation of neuroimaging data. Algorithms trained on clinical records can now assist in identifying suicide risk, treatment-resistant depression, or medication side effects more efficiently than traditional tools. These technologies are also supporting the development of personalized treatment plans based on symptom clusters and patient histories. While promising, the field faces ethical questions surrounding privacy, bias in datasets, and clinical accountability. With cautious integration, AI can augment psychiatric care and increase access to timely, individualized support for patients worldwide.
Title : The Storm Within: Neuropsychological Insights into Dysregulation and Substance Use in the Adolescent Brain.
Ann Marie Leonard Zabel, Curry College, United States
Title : Sexualizing anxiety and anxiolytic sex: Misattribution of arousal
Sam Vaknin, CIAPS, Cambridge, United Kingdom
Title : Workplace and occupational mental health: Supporting well-being at work
Sindu Padmanabhan, Bharathiar University, India
Title : Resilience in counseling: Processing grief for the addictions counselor
Kayla Albers, Hazelden Betty Ford Graduate School, United States
Title : Resilience in counseling: Processing grief for the addictions counselor
Jorja Jamison, Hazelden Betty Ford Graduate School, United States
Title : Identity restoration as a missing variable in relapse prevention
Andrew Drasen, A Vision of Hope Media, United States