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. The Master Data Plan (MDP) will collect and analyze data from diverse sources, utilize AI algorithms for risk factor identificat io n, treatment planning, and relapse prediction, and provide a patient-facing chatbot for information and support. This platform will integrate patient data, support healthcare professionals, and empower patients, thereby enhancing care accessibility, personalizing treatment plans, and optimizing care pathways. Robust data governance measures will ensure ethical and secure data management. Results: Anticipated outcomes include enhanced care accessibility and efficiency, personalized treatment plans leading to improved patient outcomes, reduced waiting lists, heightened patient engagement, and increased awareness of EDs with improved resource allocation. Discussion: This project signifies a pivotal shift towards data-driven, patient-centered ED care in Italy. By integrat ing AI and promoting collaboration, it seeks to redefine mental healthcare standards and foster better well- being among individuals with EDs.

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. This review aims to focus on the use of digital technologies measuring symptom severity and predicting treatment outcomes for individuals with mood disorders. Methods: Two databases (PubMed and APA PsycINFO) were searched to retrieve papers published from January 1, 2013, to July 30, 2023, on the use of digital devices in persons with MDD. All papers had to meet specific inclusion criteria, which resulted in the inclusion of 12 articles. Results: Research on digital biomarkers confronts four core aspects: (I) predicting diagnostic status, (II) assessing symptom severity and progression, (III) identifying treatment response and (IV) monitoring real-word and ecological validity. Different wearable technologies have been applied to collect physiological, activity/sleep, or subjective data to explore their relationships with depression. Discussion: Depression's stable rates and high relapse risk necessitate innovative approaches. Wearable devices hold promise for continuous monitoring and data collection in real world setting. Conclusion: More studies are needed to translate these digital biomarkers into actionable interventions to improve depression diagnosis, monitoring and management. Future challenges will be the applications of wearable devices routinely in personalized medicine.