In the past ten years, Artificial Intelligence (AI) has made tremendous advancements, enabling AI systems to perform complex tasks, from visual recognition to natural language processing, that were once thought to be the sole domain of human intelligence. Yet, there’s a significant challenge: the development of AI systems capable of understanding and addressing mental health issues with emotional intelligence. This emerging field seeks to give AI the ability to understand and empathize with human emotions, a nuanced task with no fixed boundaries and considerable individual variation. The integration of emotionally intelligent AI into digital health, particularly in mental health, is a groundbreaking step towards improving emotional well-being, which is vital given the worldwide increase in mental health problems. For mental health professionals, the potential of AI to analyze vast amounts of health data from wearables and health-focused apps presents unique opportunities. It offers the ability to more personalized and responsive care, potentially revolutionizing how clinicians assess, diagnose, and treat mental health conditions. This technology could assist in early detection of mental health issues, provide continuous monitoring, all of which could significantly enhance the efficacy of mental health treatments.
Recent technological advances, particularly in machine learning, affective computing, and natural language processing, have opened new avenues for emotionally intelligent AI in the digital mental health field. Despite these advancements, the integration of these cutting-edge technologies into everyday mental health practice remains a crucial challenge to be addressed. This Research Topic seeks to bridge this gap by showcasing how these methodologies can assist mental health professionals in assessment, diagnosis, and treatment planning, thereby improving patient outcomes. To this end, the collection will highlight novel applications in areas like unimodal/multimodal affect recognition, and AI-driven emotion recognition tools in therapy sessions to enhance personalized treatment. These applications not only advance the field of affective computing but also demonstrate the potential of emotionally intelligent AI to enhance psychological well-being and improve mental health outcomes. By showcasing these advancements, our goal is to inspire both AI researchers and mental health clinicians to explore and adopt these technologies in their respective fields.
This Research Topic aims to explore the convergence of artificial intelligence, psychology, and digital health, with a focus on the integration and impact of emotionally intelligent AI in mental health and digital healthcare. Our primary goal is to show how these areas can help toward more comprehensive and emotionally intelligent digital health solutions. We welcome original research articles, review papers, case studies, and all other article types accepted by Frontiers in Digital Health, that include (but are not limited to) the following topics:
1. Exploring how psychological theories can enhance emotionally intelligent AI applications in health interventions, and their role in developing more effective, human-centric AI solutions
2. Design, development, and practical integration of emotionally intelligent AI systems in mental health care, including: a) algorithmic approaches and challenges in healthcare integration, and b) applications in remote mental health care delivery, teletherapy, and digital mental health.
3. Ethical implications of emotionally intelligent AI in healthcare, focusing on identifying and mitigating biases for equitable treatment across diverse populations.
4. Evaluations of emotionally intelligent AI applications in real-world settings, assessing patient outcomes and user experiences.
5. Development and effectiveness of AI-driven personalized care methodologies, including how these approaches can complement traditional therapeutic methods and enhance treatment outcomes.
6. Novel methodologies in emotion recognition area (e.g., facial expression, speech/physiological emotion recognition) and their applications in mental health assessment and treatment.
7. Examining how emotionally intelligent AI can be used to improve patient engagement and adherence to mental health treatment plans.
Keywords:
Emotion Intelligence, Mental Health, Emotion Recognition, Ethical AI, Psychological Well-being, Personalization
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
Recent technological advances, particularly in machine learning, affective computing, and natural language processing, have opened new avenues for emotionally intelligent AI in the digital mental health field. Despite these advancements, the integration of these cutting-edge technologies into everyday mental health practice remains a crucial challenge to be addressed. This Research Topic seeks to bridge this gap by showcasing how these methodologies can assist mental health professionals in assessment, diagnosis, and treatment planning, thereby improving patient outcomes. To this end, the collection will highlight novel applications in areas like unimodal/multimodal affect recognition, and AI-driven emotion recognition tools in therapy sessions to enhance personalized treatment. These applications not only advance the field of affective computing but also demonstrate the potential of emotionally intelligent AI to enhance psychological well-being and improve mental health outcomes. By showcasing these advancements, our goal is to inspire both AI researchers and mental health clinicians to explore and adopt these technologies in their respective fields.
This Research Topic aims to explore the convergence of artificial intelligence, psychology, and digital health, with a focus on the integration and impact of emotionally intelligent AI in mental health and digital healthcare. Our primary goal is to show how these areas can help toward more comprehensive and emotionally intelligent digital health solutions. We welcome original research articles, review papers, case studies, and all other article types accepted by Frontiers in Digital Health, that include (but are not limited to) the following topics:
1. Exploring how psychological theories can enhance emotionally intelligent AI applications in health interventions, and their role in developing more effective, human-centric AI solutions
2. Design, development, and practical integration of emotionally intelligent AI systems in mental health care, including: a) algorithmic approaches and challenges in healthcare integration, and b) applications in remote mental health care delivery, teletherapy, and digital mental health.
3. Ethical implications of emotionally intelligent AI in healthcare, focusing on identifying and mitigating biases for equitable treatment across diverse populations.
4. Evaluations of emotionally intelligent AI applications in real-world settings, assessing patient outcomes and user experiences.
5. Development and effectiveness of AI-driven personalized care methodologies, including how these approaches can complement traditional therapeutic methods and enhance treatment outcomes.
6. Novel methodologies in emotion recognition area (e.g., facial expression, speech/physiological emotion recognition) and their applications in mental health assessment and treatment.
7. Examining how emotionally intelligent AI can be used to improve patient engagement and adherence to mental health treatment plans.
Keywords:
Emotion Intelligence, Mental Health, Emotion Recognition, Ethical AI, Psychological Well-being, Personalization
Important Note:
All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.