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Artificial intelligence (AI) in mental health provides numerous benefits and opportunities for patients and clinicians. Broadening patient access to mental health treatment, improving engagement and quality of care, and providing a better work-life for providers are just a few advantages of AI mental health.
Kellogg and Sadeh-Sharvit (2022) examined the potential challenges—and possible solutions—associated with AI in mental health, focusing on how these technologies impact mental health providers and the care they deliver. The researchers identified three categories of current AI mental health technologies that clinicians can use in treatment: Automation, Engagement, and Clinical Decisions Support Technologies.
Automation in AI Mental Health
Automation is an AI mental health technology that automates healthcare management to enhance delivery or reduce administrative costs, typically accessed through computer vision and machine learning (ML) systems.
The benefits of automation include greater patient access to treatments, early screenings, and better quality of work life for clinicians. Potential automation challenges can include clinicians’ complacency, leading to clinicians committing errors. Additionally, clinicians may believe that AI in mental health will ultimately replace them, making live clinicians obsolete one day.
Automation complacency can be addressed through risk assessment to identify aspects of the technology that can lead to errors. Regarding clinicians having doubts about one day being replaced by AI, leaders in the field can emphasize the augmenting nature of AI as a way of improving rather than replacing practitioners. They emphasize the importance of human involvement in developing and shepherding the growth of these technologies.
Engagement in AI Mental Health
Engagement is an AI mental health technology that facilitates engagement with patients using chatbots and intelligent agents who use natural language processing (NLP). This technology has many benefits, such as providing prevention and intervention resources, increasing patient access to care, and providing support between treatment sessions. However, challenges that can arise include patients forming a therapeutic alliance with the chatbot, which previous research has found to be the case among some patients, increasing the therapeutic alliance over time. Clinicians may have concerns about this adversely impacting the patient-clinician therapeutic alliance. Patients may also develop an excessive reliance on AI mental health technology.
Solutions for these concerns related to engagement, developers can design AI mental health technologies to identify if the user is becoming too dependent on the chatbot, for instance, or notify the patient when they should seek help from their provider and direct them to this option.
Clinical Decision Support Technologies in AI Mental Health
Clinical Decision Support Technologies analyze structured information using ML algorithms and neural networks to aid early detection and diagnosis. This tool provides many significant benefits to the treatment process, such as assisting in the provider’s ability to detect a mental health diagnosis at earlier stages, thus enabling clinicians to intervene and offer uniquely tailored treatment. However, a challenge is the lack of clinician trust in the AI mental health technology’s ability to support accurate diagnoses. These concerns can reportedly be mitigated by involving clinicians in evaluating these systems. This human-in-the-loop approach allows mental health providers to be involved in developing and using AI mental health.
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