The mental health crisis is real, and it’s not going away any time soon. That’s why many are turning to artificial intelligence (AI) to help address the issue. In this blog post, we’ll explore the promise and challenges of using AI for mental health and how this technology can be used as a tool in helping those in need.
Introduction to AI for Mental Health
Introducing AI into mental health has the potential to transform healthcare and neurobiological research. Big data offers promise in the field of mental health and plays an important part when it comes to automation, analysis, and prediction. Artificial intelligence (AI) technology holds both great promises to improve patient access, engagement, and quality, as well as potential pitfalls due to its complexity and the rise of data in healthcare. It is essential to understand the implications of using AI in mental health from an interdisciplinary perspective in order to balance the anticipated benefit of psychiatric applications of AI with the need to promote epistemic humility in clinical judgments.
The Potential of AI for Mental Health
The Potential of AI for Mental Health has been argued to increase patient access, engagement, and quality of care. Non-verbal signs interpretation and structured interviews with mental health practitioners are two ways in which AI can be used to identify potential mental health problems. Additionally, AI-based applications can help reduce bias in clinical judgments and increase patient access and engagement. However, it is important to balance the anticipated benefit of psychiatric applications of AI with epistemic humility in clinical judgments.
Non-Verbal Signs Interpretation in Mental Health
Non-Verbal Signs Interpretation is a key component of the AI therapist’s capabilities. AI can interpret non-verbal signs, such as facial expressions, posture, or gestures, with accuracy and precision. Computer vision for imaging data analysis and understanding non-verbal cues is also being used to help detect, manage, and treat mental health issues. AI decision support technologies use past data to make predictions, while explainable Artificial Intelligence-Based Apps [JMIR 20221Malik T, Ambrose AJ, Sinha C. Evaluating User Feedback for an Artificial Intelligence-Enabled, Cognitive Behavioral Therapy-Based Mental Health App (Wysa): Qualitative Thematic Analysis. JMIR Human Factors. 2022 Apr;9(2):e35668. DOI: 10.2196/35668. PMID: 35249886; PMCID: PMC9044157] enable structured interviews with mental health practitioners. Despite the potential benefits of using AI in mental health care, there are also potential risks of bias in AI systems that must be addressed through epistemic humility in clinical judgments.
Potential for Bias in AI Systems
Bias in AI systems is a burgeoning concern for mental health practitioners, as automated decision support systems (DSS) are increasingly being used to make decisions about patient care. This is known as automation bias and can have dangerous consequences when the DSS is wrong or fails or the presenting problem is subtly unique. To mitigate this, ethical considerations must be taken into account when designing AI systems and developing algorithms. This includes ensuring informed consent to use data, avoiding algorithmic discrimination, and focusing on developing digital platforms with explainable artificial intelligence–based apps to enhance resilience and guide the decision-making process. AI-driven predictions could therefore sway clinicians’ own assessments of risk, so it is essential to weigh the anticipated benefit of psychiatric applications of AI against any potential for bias or discrimination.
Increasing Patient Access and Engagement
Increasing patient access and engagement through the integration of AI technologies into mental health is a potential solution for key issues in mental health, such as delayed, fragmented, and inaccessible care. AI technologies can help alleviate the administrative burden and address the clinician shortage. By increasing patient access, AI-based applications can improve outcomes, reduce costs, and facilitate better communication between mental health practitioners and their patients. Further, these tools can offer clinicians an opportunity to interact with patients in a more personalized manner.
Explainable Artificial Intelligence-Based Apps
Explainable Artificial Intelligence-Based Apps can offer a variety of potential benefits for mental health, such as increasing patient access, engagement, and quality of care. AI platforms can result in more successful medication adherence for patients with schizophrenia, as well as help address social isolation, transportation and mobility, mental and physical health, caregiver burden, and end-of-life issues. However, creators of AI technology should be aware of the potential for bias in AI systems that may arise from insufficient and poor-quality data. Therefore, it is important to focus on developing digital platforms with explainable AI-based apps in order to enhance resilience and guide the appropriate use of AI for mental health applications.
Mental health chatbots have become popular in recent years; three prominent ones are Woebot [Woebot Labs 2Woebot Labs, Woebot [Online]. Available: https://woebot.io/.], Wysa [mfn]3[/mfn] and Tess [mfn]4[/mfn].
- Woebot helps clients with depression and anxiety by delivering cognitive behavioral therapy via brief conversations and mood tracking. Research has shown that after two weeks of using Woebot, the group experienced a significant reduction in depression compared to the control group, who were given information about depression but did not see any overall improvement. Both groups, however, reported a reduction in anxiety.
- Wysa also employs CBT to aid people with depression, and preliminary studies revealed that users with high engagement had greater average improvements on PHQ-9 than those with low engagement. 68% of user-provided feedback responses were positive toward the app experience.
- Of all the mental health chatbots available, Tess appears to have the most published research so far.
Structured Interviews with Mental Health Practitioners
Based on structured interviews with mental health practitioners, the potential risks and challenges of using AI for mental health applications were further understood. Mental health clinicians gained a better understanding of the uses, benefits, and challenges of AI in clinical scenarios, as well as the need for balancing the anticipated benefit of psychiatric applications of AI with epistemic humility in clinical judgments.
Balancing the Anticipated Benefit of Psychiatric Applications of AI
Balancing the Anticipated Benefit of Psychiatric Applications of AI is crucial to ensure that the potential of AI for mental health is met. Studies have shown that AI technology can be used to enhance medical capacity, improve research, and help mental health professionals do their jobs better. Additionally, AI solutions can also encourage patients to answer truthfully and accept support more objectively, use big data more efficiently than humans, and interpret non-verbal signs. However, it is important to consider the need for epistemic humility in clinical judgments as well as developing digital platforms with explainable artificial intelligence-based apps to enhance resilience and guide structured interviews with mental health practitioners. Ultimately, this will ensure that the promise and challenges in using AI for mental health are addressed appropriately.
Epistemic Humility in Clinical Judgements
Balancing the Anticipated Benefit of Psychiatric Applications of AI with the need to promote epistemic humility in clinical judgments is vital. A commitment to epistemic humility can help promote judicious clinical decision-making at the interface of big data and AI. It can also enable medical students to practice engaging with complexity and ambiguity in the clinical encounter and develop the intellectual humility needed for accurate judgments of recognition, which is associated with better mental health outcomes. Furthermore, AI technologies are likely to affect physicians’ clinical judgment, making it important to consider the ethical issues that arise in the digital mental health response to COVID-19. Ultimately, understanding how AI could be used to improve human moral judgments and increase patient access and engagement is critical for realizing the potential of AI for mental health.
Concluding Thoughts on AI for Mental Health
Concluding Thoughts on AI for Mental Health, while it holds great promise to transform mental healthcare and provide better treatments, there are potential pitfalls that creators of AI mental healthcare technology should be wary of. These include bias that may come with insufficient and poor quality data and the potential for non-verbal signs interpretation to be misinterpreted. However, by increasing patient access and engagement with AI-based apps and structuring interviews with mental health practitioners to balance the anticipated benefit of psychiatric applications of AI, we can create an environment that is conducive to epistemic humility in clinical judgments. Ultimately, this will ensure a greater understanding of biological categories of psychiatric disorders and an improved quality of life for those affected.
- 1Malik T, Ambrose AJ, Sinha C. Evaluating User Feedback for an Artificial Intelligence-Enabled, Cognitive Behavioral Therapy-Based Mental Health App (Wysa): Qualitative Thematic Analysis. JMIR Human Factors. 2022 Apr;9(2):e35668. DOI: 10.2196/35668. PMID: 35249886; PMCID: PMC9044157
- 2Woebot Labs, Woebot [Online]. Available: https://woebot.io/.