AI, Robotics, and Digital Health, Volume II: Future of Mental Health Care: AI-driven Digital Phenotyping and Predictive Analytics

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About this Research Topic

Submission deadlines

  1. Manuscript Summary Submission Deadline 30 November 2025 | Manuscript Submission Deadline 31 March 2026

  2. This Research Topic is currently accepting articles.

Background

This Research Topic is led by Changiz Mohiyeddini and Wing-Yue Geoffrey Louie and is part of the AI, Robotics, and Digital Health series:

Volume I: Transformative Potential of AI and Robotics in Mental Health: Insights into Hybrid Care Paradigms

Volume III: Towards Equitable AI in Mental Health: Overcoming Bias, Promoting Ethics, and Fostering Trust

The convergence of artificial intelligence (AI) and robotics with digital health is redefining psychological and medical care. This interdisciplinary Research Topic interrogates how these technologies reshape health psychology and healthcare—from AI and robotic mental health interventions to ethical dilemmas in algorithmic care.

The integration of artificial intelligence (AI) into digital phenotyping and predictive analytics marks a significant development in mental health care. With advancements in wearables, smart devices, and ambient sensors, there is an unprecedented ability to collect and analyze vast volumes of real-time health and behavioral data. This data holds promise for proactive mental health management through earlier detection of crises and the facilitation of personalized preventive care. Despite progress, the deployment of AI in mental health prediction warrants a critical examination of its techniques, risks, and ethical implications.

This Research Topic aims to explore the capabilities and challenges of AI-powered digital phenotyping and predictive analytics in mental health. It seeks to identify cutting-edge machine learning methods for analyzing multimodal data streams to predict mental health crises or states, investigate reliability in predicting individualized risk for proactive intervention, and address data governance issues regarding ownership and privacy. Furthermore, the aim is to tackle questions on algorithmic biases, the validation of predictive models across diverse populations, and the translation of predictive insights into actionable clinical care. Special consideration is given to ethical frameworks to safeguard patient autonomy and prevent misuse or discrimination.

To gather further insights into these advancements, the scope of this Research Topic includes, but is not limited to, the following themes:

• Innovations in next-generation digital phenotyping techniques

• Data governance considerations: ownership, privacy, and consent frameworks

• Addressing biases and ensuring generalizability in AI predictors

• Pathways for translating predictive insights into actionable care

• Designing clinician and patient-friendly decision support tools

This Research Topic welcomes submissions of original research articles, reviews, and empirical studies. The aim is to deepen understanding of the technical, ethical, and practical issues surrounding AI in mental health prediction, thereby shaping the future of preventive, personalized, and data-driven psychiatric care.

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Keywords: AI-powered digital phenotyping, Predictive analytics in mental health, Algorithmic bias and fairness in mental health AI, Data governance and privacy in health AI, Machine learning for mental health prediction

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