Digital Interventions for the Rehabilitation of First-Episode Psychosis: An Integrated Perspective.

Background: The integration of digital health technologies has transformed mental healthcare, particularly for young adults with First-Episode Psychosis (FEP). Digital interventions, such as telepsychiatry and mobile applications, address barriers like social stigma, restricted access to services, and the urgency of timely care.

Methods: A systematic literature review was conducted using PubMed and APA PsycINFO.

An advanced Artificial Intelligence platform for a personalised treatment of Eating Disorders.

Introduction: Eating Disorders (EDs) affect individuals globally and are associated with significant physical and mental health challenges. However, access to adequate treatment is often hindered by societal stigma, limited awareness, and resource constraints.

Methods: The project aims to utilize the power of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve EDs diagnosis and treatment.

Optimizing and Predicting Antidepressant Efficacy in Patients with Major Depressive Disorder Using Multi-Omics Analysis and the Opade AI Prediction Tools.

According to the World Health Organization (WHO), major depressive disorder (MDD) is the fourth leading cause of disability worldwide and the second most common disease after cardiovascular events. Approximately 280 million people live with MDD, with incidence varying by age and gender (female to male ratio of approximately 2:1). Although a variety of antidepressants are available for the different forms of MDD, there is still a high degree of individual variability in response and tolerability.

A narrative review of digital biomarkers in the management of major depressive disorder and treatment-resistant forms.

Introduction: Depression is the leading cause of worldwide disability, until now only 3% of patients with major depressive disorder (MDD) experiences full recovery or remission. Different studies have tried to better understand MDD pathophysiology and its resistant forms (TRD), focusing on the identification of candidate biomarkers that would be able to reflect the patients' state and the effects of therapy. Development of digital technologies can generate useful digital biomarkers in a real-world setting.