Artificial Intelligence & Mental Health

As widespread as AI has become, confusion and uncertainty exist around how AI technologies work. Developing an understanding of these technologies will enable informed decision-making on regulatory and legislative actions, support decisions pertaining to their adoption and application, foster responsible development and usage, and empower individuals to engage in discussions about ethical considerations, fairness, and bias to ensure that AI benefits society as a whole.

To help facilitate AI literacy in policy and practice discussions, the Meadows Institute developed an introductory primer on AI in mental healthcare, as part of an Augmented Intelligence and Mental Health Primer Series.

AI is supporting and expanding new opportunities in mental health to ensure better access and outcomes for patients. One promising area is measurement-informed care (MIC), which informs clinical care by improving treatment decisions and monitoring a patient’s progress over time. Starting with the initial screening of patients, MIC involves the repeated, systematic use of validated measures during clinical encounters to inform decision-making about treatment, thereby supporting – not replacing – clinical judgment. How data are measured, assessed, and used in mental health contexts is constantly evolving. AI represents the next step in the evolution of measuring and monitoring mental health care. The Meadows Institute completed research to assess the opportunities for AI to transform MIC in mental health.

While AI holds immense potential to improve mental health care, it also presents ethical and safety challenges that require careful consideration of specific evaluations. The Readiness Evaluation for AI Deployment and Implementation for Mental Health (READI) framework, recently developed by Stanford University’s Human-Centered Artificial Intelligence Center, provides a starting point for evaluating whether AI mental health applications are ready for clinical deployment. The READI framework emphasizes the following:

• Safety
• Privacy and confidentiality
• Equity
• Engagement
• Effectiveness
• Implementation considerations

One of the most pressing ethical and fairness issues in AI today is bias. Bias can manifest itself in various ways, including dataset bias, where training data are flawed due to faulty underlying assumptions or the underrepresentation of certain groups, leading to skewed AI outcomes.

To address bias in AI, it is crucial to start with diverse and representative training data that accurately reflect the real-world populations and scenarios the AI system will encounter. This is especially important in the mental health space, as biased AI systems may lead to misdiagnosis, lack of treatment, and poor care experiences. Continuous monitoring, auditing, and transparency throughout the AI development lifecycle, combined with diverse teams and ethical guidelines, are essential to preventing and mitigating the risk of bias in AI and promoting fairness in its application. To understand opportunities to mitigate bias in AI when applied to mental health care.

Meadows Institute Augmented Intelligence and Mental Health Primer Series Resources

• Mental Health and AI: An Introductory Primer
• Opportunities and Considerations for Augmented Intelligence in Measurement-Informed Care in Mental Health

AI in Mental Health Care Positive Use Cases
Artificial intelligence (AI) is reshaping health care and offers an unprecedented opportunity to transform mental health services when used alongside clinicians. Developing a foundational understanding of how AI works and how it can be applied in mental health care can help clinicians, policymakers, and the public more effectively engage in decisions about its adoption and regulation. Explore the Meadows Institute’s use case explainers to build an understanding of the current and potential roles of AI across the mental health continuum.Lindsey Engelman, MA; MSCRP, Director for Innovation, at lengelman@mmhpi.org.

For more information about AI in Mental Health Care, contact:
Lindsey Engelman, MA; MSCRP, Director for Innovation, at lengelman@mmhpi.org.