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 : Integrating bibliopoetry therapy and digital health technologies for inflammation management: A neuropsychosomatic perspective
Nile Stanley, University of North Florida, United States
Title : Reduction of chronic neuropathic pain by a THC-CBD combination capsule: ongoing pilot study
Frederick J Goldstein, Philadelphia College of Osteopathic Medicine, United States
Title : Suicide prevention strategies for the law enforcement profession
Stephanie Schweitzer Dixon, SSD Consulting, United States
Title : The storm within: Neuropsychological insights into dysregulation and substance use in the adolescent brain
Ann Marie Leonard Zabel, Curry College, United States
Title : The three-second neuroshift™: Reset before relapse for addiction & emotional dysregulation
Edie Raether, NeuroShifts and Wings for Wishes Academy, United States
Title : TAMAR (Trauma, Addiction, Mental Health, and Recovery)
Angelo Reynolds, Transformers Academy, LLC, United States