About this Research Topic
The prevalence of mental health illness is worldwide on the rise. Illnesses such as depression and anxiety have severe effects on the quality of life of individuals, while at the same time indirectly affecting their close circles. The often long-term nature of treatments poses strains on the medical system, and the high heterogeneity of needs and manifestations challenge medical professionals and impact success. In addition, medical professionals always need to factor in the possibility that a treatment does not do the right thing for the client. Against this background, there is a great potential for artificial intelligence solutions to support clients and practitioners in maintaining and improving their mental wellbeing.
Awareness of experienced affective states, somatic responses and adopted behaviours plays an important role in understanding, diagnosing and offering support or customizing treatment. For example, the assessment of overt behaviours such as vocal inflections, gaze and gestures, as well as physiological responses can complement traditional questionnaires to aid diagnostics. Various digital tools such as VR exposure therapy environments or m-health applications can increase their adaptability and personalization by gaining awareness of the user’s affective states. Clinicians can potentially better monitor the progress of patients and the effects of therapies. Further, the design of supportive interventions that make use of AI techniques plays a critical role in maintaining and improving mental wellbeing. While previous efforts brought significant advances in many directions related to supporting mental health with AI, the adoption of AI-related solutions among the various stakeholders involved still is limited.
Digital tools based on AI techniques have the capability to dramatically improve the provision of and engagement in mental care. Such tools and techniques allow for the provision of instant online decision support to people suffering from mental disorders. For the client, this may lead to a reduction of psychological distress and an increase in well-being. For medical professionals, such tools enable the monitoring of treatment progress and success at-a-distance, with the possibility to implement changes to the treatment plan if necessary. In addition, digital tools can be used to offer the client a form of mental care that is tailored to personal needs and preferences. Whereas there exists wide agreement on the promise of AI-based digital tools for a personalized medicine for the mind, the number of use cases and empirical studies in support of specific technological solutions is scarce.
This call aims to bring together contributions focusing on AI to stimulate and promote mental wellbeing. Interdisciplinary efforts are particularly appreciated. We welcome contributions related to:
- AI for maintaining mental wellbeing
- AI for mental health diagnostics: using objective behavioral or biological indicators in addition to standard questionnaires, assessment of textual excerpts, ecological momentary assessment.
- Affect recognition from multiple modalities and multi-modal fusion
- Context awareness
- Dialogue systems, chatbots, virtual humans in the context of mental health support
- Designing for mental health - Treatment / support / interventions - User-technology interaction
- Personalization and adaptation
- Short and long term user studies
- Adoption of technology
- Ethics & AI trustworthiness
Awareness of experienced affective states, somatic responses and adopted behaviours plays an important role in understanding, diagnosing and offering support or customizing treatment. For example, the assessment of overt behaviours such as vocal inflections, gaze and gestures, as well as physiological responses can complement traditional questionnaires to aid diagnostics. Various digital tools such as VR exposure therapy environments or m-health applications can increase their adaptability and personalization by gaining awareness of the user’s affective states. Clinicians can potentially better monitor the progress of patients and the effects of therapies. Further, the design of supportive interventions that make use of AI techniques plays a critical role in maintaining and improving mental wellbeing. While previous efforts brought significant advances in many directions related to supporting mental health with AI, the adoption of AI-related solutions among the various stakeholders involved still is limited.
Digital tools based on AI techniques have the capability to dramatically improve the provision of and engagement in mental care. Such tools and techniques allow for the provision of instant online decision support to people suffering from mental disorders. For the client, this may lead to a reduction of psychological distress and an increase in well-being. For medical professionals, such tools enable the monitoring of treatment progress and success at-a-distance, with the possibility to implement changes to the treatment plan if necessary. In addition, digital tools can be used to offer the client a form of mental care that is tailored to personal needs and preferences. Whereas there exists wide agreement on the promise of AI-based digital tools for a personalized medicine for the mind, the number of use cases and empirical studies in support of specific technological solutions is scarce.
This call aims to bring together contributions focusing on AI to stimulate and promote mental wellbeing. Interdisciplinary efforts are particularly appreciated. We welcome contributions related to:
- AI for maintaining mental wellbeing
- AI for mental health diagnostics: using objective behavioral or biological indicators in addition to standard questionnaires, assessment of textual excerpts, ecological momentary assessment.
- Affect recognition from multiple modalities and multi-modal fusion
- Context awareness
- Dialogue systems, chatbots, virtual humans in the context of mental health support
- Designing for mental health - Treatment / support / interventions - User-technology interaction
- Personalization and adaptation
- Short and long term user studies
- Adoption of technology
- Ethics & AI trustworthiness
Keywords: Affective Computing, Mental Health, Artificial Intelligence, Machine Learning, Digital Health, Human-technology Interaction, Multimodal Signals, User Experience, Behaviour modelling, Behavioural change programs, Digital Nudging, Recommender Systems, Persuasive Technologies
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.
