درخت حوزه‌های تخصصی

Background: The integration of artificial intelligence into psychodynamic therapy presents a transformative opportunity to enhance mental health interventions.Aims: By consolidating evidence from diverse studies, this research underscores the theoretical and practical implications of psychodynamic AI systems and their potential to reshape therapeutic practices.Methodology: This study employs a meta-analytic approach to synthesize findings from existing research, focusing on the application of psychodynamic principles—such as unconscious processes, relational dynamics, and symbolic meaning—in AI-driven systems. Despite the dominance of cognitive-behavioral frameworks in AI therapeutic tools, psychodynamic approaches offer unique potential for fostering deeper emotional engagement and interpersonal understanding.Results: The analysis highlights advancements in AI-based systems designed to emulate psychodynamic processes and explores challenges, including technical limitations and ethical concerns. The findings emphasize the need for future research to refine AI's capacity to address complex psychodynamic phenomena while adhering to ethical standards, ensuring accessibility, and enhancing emotional intelligence.Conclusions: Ultimately, this work advocates for a balanced integration of psychodynamic principles with AI technologies to achieve scalable and effective mental health solutions.

Background: Misinformation has undergone significant transformations over the past few decades, evolving from relatively simple text-based fake news articles to highly sophisticate AI-driven content such as deepfakes and other forms of manipulated media.Aims: This paper traces the historical development of misinformation, its increasing reliance on Artificial Intelligence (AI), and the potential future trajectories of disinformation as AI technologies advance.Methodology: We begin by examining the shift from traditional text-based disinformation campaigns, often propagated via social media platforms, to more immersive and persuasive forms of AI-generated media.Discussions: We discuss how AI techniques, such as Generative Adversarial Networks and Natural Language Processing, have revolutionized the landscape of false information, allowing for the automation of misinformation production and its widespread dissemination at an unprecedented scale. Furthermore, this paper investigates the role of social media algorithms in amplifying disinformation, demonstrating how these platforms, originally designed to prioritize user engagement, inadvertently aid in the spread of false information by promoting sensationalized or emotionally charged content. Through an in-depth analysis of case studies, including the COVID-19 pandemic and the 2020 U.S. elections, this paper highlights the dangers posed by AI-generated misinformation, particularly deepfakes, which are becoming increasingly difficult to detect, even by advanced AI systems. The implications of this shift for democratic processes, public trust, and societal cohesion are profound. This paper also explores the ethical dilemmas posed by AI-driven misinformation and presents potential solutions through the lens of AI-enhanced detection technologies and policy interventions. Lastly, this paper emphasizes the urgent need for interdisciplinary cooperation between policymakers, technologists, and media organizations to mitigate the harmful impacts of AI-driven misinformation while preserving the integrity of information in the digital age.Conclusions: By exploring both technological and regulatory approaches, a comprehensive framework for understanding the evolving threat of AI-driven disinformation is essential and pathways for future research in this critical area is suggested.