A. Wojtera

AI IN PSYCHIATRIC DIAGNOSTICS – A REVIEW

Introduction: Artificial intelligence (AI) is increasingly being used in psychiatry, with side effects on solutions stemming from the subjectivity of diagnosis, limited care, and biological complexity, which is subject to threats. Mental disorders affect 293 million people worldwide and pose a burden on human health [9]. Aim: The aim of this review is to summarize the current state of knowledge on AI applications in psychiatric diagnostics, with specific focus on: (1) analysis of communication traffic of AI algorithms, (2) analysis of the results of AI-based primary control, (3) extension of methodological and ethical implications, and (4) extension of research. Methods: A review of the research literature was conducted in the field of Basic Language Processing (NLP) in digital phenotyping, AI-assisted neuroimaging, and the ethical and legal implications of implementing these technologies. Meta-analyses, specific reviews, and original empirical studies completed between 2015 and 2026 were analyzed. Results: A meta-analysis reported a cumulative AI diagnostic accuracy of 85% and a therapeutic efficacy of 84% in specific applications [8]. NLP enabled independent assessment, achieving an 86% (AUC 0.93) in studies on psychosis risk states [18]. Chatbots (Woebot, Wysa, Youper) demonstrate the consequences of problem occurrence and anxiety [9]. A review of 555 neuroimaging models revealed that 83.1% of the symptoms appear as a consequence rather than being triggered by a utility [32]. The most important ethical concerns were identified, including algorithm opacity ("black box"), liability, and data privacy [54, 56, 61]. Conclusions: AI in psychiatric diagnostics has demonstrated transformative potential, particularly in the areas of NLP and digital phenotyping, but current neuroimaging models require methodological improvements. The development of comprehensive ethical frameworks and extensions, simple algorithms, and model validation in large, population-based cohorts are essential. The ultimate success of AI in psychiatry will depend on striking a balance between technological innovation and respect for fundamental ethical values, while maintaining a paramount clinical role in diagnostic and therapeutic procedures.

Innovative Technologies in Social

THE ROLE OF INNOVATIVE TELEMEDICINE TECHNOLOGIES IN DIABETES CARE: A NARRATIVE REVIEW OF METABOLIC, BEHAVIORAL, AND SOCIAL OUTCOMES

Introduction: Diabetes mellitus remains a major public health challenge worldwide, requiring continuous monitoring, long-term treatment, and active patient participation. Recent advances in telemedicine, continuous glucose monitoring systems, mobile health applications, and artificial intelligence have created new opportunities for remote and personalized diabetes management. Aim: This narrative review evaluated the impact of telemedicine and digital health technologies on metabolic control, treatment adherence, self-management behaviors, and psychosocial outcomes among adults with type 1 and type 2 diabetes mellitus. Materials and Methods: A narrative review of the literature was conducted using keywords in the PubMed database. Recent studies involving adult patients with type 1 and type 2 diabetes were analyzed. Attention was paid to outcomes related to glycated hemoglobin (HbA1c), continuous glucose monitoring metrics, treatment adherence, patient engagement, self-management, and quality-of-life indicators. Results: Most reviewed studies demonstrated that telemedicine interventions significantly improved glycemic control, particularly by reducing HbA1c levels. Digital care models incorporating continuous glucose monitoring increased Time in Range and reduced hyperglycemic exposure. Telemedicine was also associated with improved adherence to treatment recommendations, greater patient engagement, enhanced self-management competencies, and positive psychosocial outcomes, including improved quality of life and reduced diabetes-related distress. The greatest benefits were observed in patients with poor baseline metabolic control and in programs integrating remote monitoring with structured educational support. Conclusions: Telemedicine represents an effective and innovative complement to conventional diabetes care. Digital health technologies facilitate personalized disease management, improve patient participation in therapeutic processes, and may contribute to more accessible and sustainable healthcare delivery.