Neurocognitive disorders, including mild cognitive impairment (MCI) and dementia, are defined by cognitive decline. However, their clinical reality extends far beyond cognitive symptoms. Behavioral and psychological manifestations are highly prevalent, and often more distressing for patients and caregivers than cognitive decline itself. These symptoms accelerate functional deterioration, increase caregiver burden, and contribute to poor quality of life and higher healthcare costs.

This Special Collection seeks to highlight the behavioral, psychological, and social dimensions of neurocognitive disorders, bridging biological mechanisms, clinical management, psychosocial outcomes, and health policy. In particular, it aims to explore how the digital health and policy innovations can make a meaningful impact.

We invite research that explores how digital health, AI, and telemedicine can advance assessment, intervention, and care models, as well as studies that inform policy and service delivery. Submissions may include original research, systematic reviews, methodological papers, and policy analyses from psychiatry, neurology, psychology, geriatrics, and related fields.

Scope

1. Behavioral and Psychological Symptoms – Neuropsychiatric manifestations (e.g., depression, apathy, agitation, aggression, delusions, hallucinations) in dementia and MCI, their mechanisms, clinical correlates, and psychosocial Impact (e.g., on quality of life, caregiver burden, stigma, social participation).

2. Interventions – Pharmacological, psychosocial, lifestyle-based, and digital interventions (e.g., app-based therapies, AI-assisted care) targeting behavioral, psychological, and social needs.

3. Policy and Care Models – Service innovations and policy frameworks, including the integration of digital platforms, AI-supported systems, and telemedicine in dementia and MCI care.

Keywords: dementia, major neurocognitive disorder, mild neurocognitive disorder, mild cognitive impairment, behavioral and psychological symptoms of dementia, Neuropsychiatric manifestations

The rapid proliferation of digital technologies is transforming mental healthcare, offering innovative, more accessible, and effective solutions for a wide range of mental health conditions. The integration of Artificial Intelligence (AI) into telepsychology tools—such as web-based therapies, mobile applications, and virtual or augmented reality—opens promising avenues for treatment, prevention, and mental health promotion. While these tools have already demonstrated efficacy, AI significantly enhances their potential by enabling personalized interventions tailored to users’ evolving needs. AI-powered digital mental health solutions offer opportunities for individualized care, early detection of mental health challenges, and improved therapeutic engagement.

However, the integration of AI in telepsychology also presents substantial implementation barriers and ethical concerns. These include the need to understand AI’s underlying mechanisms, ensure effectiveness across diverse populations and settings, and address technical challenges related to digital literacy, accessibility, and integration into existing healthcare infrastructures. This collection aims to foster multidisciplinary research addressing the responsible development, implementation, and evaluation of AI-based digital interventions in mental health. We especially invite contributions that engage with the following core themes:

• Chatbots and Virtual Assistants: Their role in delivering mental health support, therapeutic guidance, and psychoeducation, as well as challenges related to trust, engagement, and clinical validation.

• Attitudes Towards and Use of AI in Mental Healthcare: Exploring perceptions, expectations, and adoption among clinicians, patients, and broader communities, including cultural and demographic factors.

• Ethics, Data Privacy, Bias Mitigation, and Preserving the Human Factor: Addressing ethical frameworks, algorithmic transparency, protection of sensitive data, equitable care, and the preservation of empathy and human connection in AI-mediated interventions.

• Natural Language Processing (NLP) and Machine Learning in Mental Healthcare: Examining the use of language-based models and predictive analytics for assessment, diagnosis, monitoring, and personalized intervention.

We encourage submissions that combine clinical, technological, ethical, and policy perspectives to critically explore both the transformative opportunities and complex challenges posed by AI in digital mental health. This collection seeks to advance responsible innovation and inclusive practices that ensure AI contributes positively to the future of mental healthcare.

Keywords:

Digital Mental Health, Artificial Intelligence (AI), Telepsychology, Ethical Challenges, e-therapy