About this Research Topic
The adoption of artificial intelligence (AI) to mental health research and clinical care has lagged behind other fields in medicine, due to the heterogeneity of diagnoses and disease states, reliance on patient-clinician rapport and trust, and dependence on behaviors rather than biomarkers. With the rise of wearable sensors, advancement in explainable AI algorithms, and growing acceptance of AI and machine learning in medicine, there has been an advent of novel AI approaches to mental health research and clinical care. The rapidly rising number of older adults worldwide presents a unique challenge for clinicians due to increased mental health needs in the setting of a dwindling clinical workforce. Leveraging AI technologies to better understand and treat mental illness in older adults will be paramount in the coming future as we advance the field and care for the growing patient population.
Artificial Intelligence has great potential to more reliably diagnose, prognose, and treat mental illnesses. However, implementation in psychiatry, namely the field of geriatric mental health, has been limited until recent times. This Research Topic will highlight a number of new applications of AI in geriatric mental health research and clinical care settings. These papers will showcase varied AI technologies and approaches (machine learning, natural language processing) as applied to a wide breadth of data-streams (sensors, electronic health records, interview data, neuroimaging) from multidisciplinary and international perspectives. In addition, there will be commentaries expanding on some of the research papers to think broadly about the policy and systemic implications. This Research Topic will explore how AI can be applied to a wide variety of clinical and research challenges, while respecting ethical boundaries and patient protections.
We are interested in Original Research, Brief Research Report, Systematic Review, Review, Mini-Review, Perspective, and Commentary articles.
We do not wish inability to cover publication fees to be a barrier for any manuscripts and authors with papers on these topics.
• Use of AI to predict treatment outcomes
• Use of AI to understand neurophysiology
• Ethical concerns regarding AI for older populations
• Use of AI for behavioral sensor data
• Use of AI for interview data
Artificial Intelligence has great potential to more reliably diagnose, prognose, and treat mental illnesses. However, implementation in psychiatry, namely the field of geriatric mental health, has been limited until recent times. This Research Topic will highlight a number of new applications of AI in geriatric mental health research and clinical care settings. These papers will showcase varied AI technologies and approaches (machine learning, natural language processing) as applied to a wide breadth of data-streams (sensors, electronic health records, interview data, neuroimaging) from multidisciplinary and international perspectives. In addition, there will be commentaries expanding on some of the research papers to think broadly about the policy and systemic implications. This Research Topic will explore how AI can be applied to a wide variety of clinical and research challenges, while respecting ethical boundaries and patient protections.
We are interested in Original Research, Brief Research Report, Systematic Review, Review, Mini-Review, Perspective, and Commentary articles.
We do not wish inability to cover publication fees to be a barrier for any manuscripts and authors with papers on these topics.
• Use of AI to predict treatment outcomes
• Use of AI to understand neurophysiology
• Ethical concerns regarding AI for older populations
• Use of AI for behavioral sensor data
• Use of AI for interview data
Keywords: Artificial Intelligence, Geriatric Mental Health, Diagnosis, Prognosis, Treatment
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.
