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 : Decoding aggression, violence, and substance use in adolescents with conduct disorders: Neurochemical pathways and interventions
Ann Marie Leonard Zabel, Curry College, United States
Title : Integrative addiction and mental health wellness lecture
John Giordano, Life Enhancement Recovery Center, United States
Title : Step one therapy
Ashton Christopher, Center for Recovery, Canada
Title : Addiction rehabilitation & recovery: Pathways to healing and resilience
Sindu Padmanabhan, Bharathiar University, India
Title : Tailoring biblio-poetry therapies using AI and biofeedback for addiction treatment
Nile Stanley, University of North Florida, United States
Title : The A-B-C of happiness coaching
Alphonsus Obayuwana, Triple-H Project LLC, United States