AI is transforming mental health care in ways you never imagined.

Mental health is a growing concern throughout the world. As per a report by the World Health Organization, approximately 1 billion people suffered from some kind of mental health disorder before the pandemic. Post Covid-19, this problem has only worsened and there has been a rise of around 25% in depression and anxiety among people. With the evolution of Artificial Intelligence (AI), mental health and its resolution is about to witness a new phase. “How does that make you feel?”  This is usually the first question asked to us by a human therapist when we share our thoughts and worries. It is not far when people would be discussing this with an AI chatbot instead of a human being. Even more, this virtual assistant might be quite effective at reducing your symptoms of worry or depression.

History of AI in mental healthcare

AI’s integration into mental healthcare began in the 1950s and 1960s during the early computing era. Researchers like Allen Newell and Herbert A. Simon developed AI models of human problem-solving, laying the foundation for symbolic AI. These early efforts set the path for simulating cognitive processes in mental health contexts. In the 1970s, Joseph Weizenbaum developed ELIZA, an early AI chatbot that simulated a Rogerian psychotherapist and showed the potential for AI in mental health. Although its responses were simple, they provided valuable insight into AI’s role in mental health interactions. By the 1980s expert systems were developed that were aimed at providing diagnostic and treatment recommendations in the psychological space. 

The late 20th century saw the creation of computerized Cognitive-Behavioral Therapy (CBT) that aimed at providing evidence-based therapy for common mental health conditions. These programs, although very old, were an important step toward using technology to improve mental healthcare accessibility. AI in the 21st century has led to advancements such as early identification of mental health problems, individualized treatment plans, virtual therapists, teletherapy, and continuous monitoring. These developments have significantly improved accessibility, effectiveness, and data-driven care in mental healthcare.

How can AI help with mental healthcare?

While there can be many uses of AI to resolve mental health conditions, the following technologies and solutions seem the most capable:

• Machine learning (ML) and deep learning (DL) provide greater accuracy in diagnosing mental health conditions and predicting patient outcomes.
• Computer vision is used for imaging data analysis and understanding non-verbal signs, such as facial expressions, gestures, eye contact, or human posture. Thus it can help in early diagnosis and detection of mental health disorders.
• Natural language processing (NLP) can be used for speech recognition and text analysis. This can create simulating human conversations through chatbot computer programs and can also be used for understanding clinical documentation.
• Generative AI is used for providing personalized, continuous support and therapy sessions via virtual assistants or chatbots. They can also be used for analyzing patient data and creating personalized therapy plans as per an individual’s requirements and preferences.

Different AI tools used in current mental healthcare

AI tools for therapy

AI tools for emotional health

Challenges in the use of AI in mental healthcare

Privacy and data security

• Mental health data is sensitive, and AI tools need to protect it.
• If someone hacks a person’s therapy sessions, they could expose their personal struggles.

Bias and fairness

• AI developers can sometimes introduce bias if they train it on unbalanced data.
• Example: If an AI system is trained mostly on data from one gender or ethnicity, it might make less accurate recommendations for people outside that group.

Lack of emotional intelligence

• AI doesn’t have empathy or emotional understanding like a human therapist.
• Example: An AI chatbot might fail to offer comfort when someone is feeling anxious, which a human therapist could provide easily.

Regulation and oversight

• Clear rules about how to use AI in mental health do not exist.
• Example: Without proper oversight, an AI tool might give wrong advice or make mistakes that could hurt someone’s mental health.

Accuracy and reliability:

• AI can sometimes make mistakes or be less accurate.
• Example: An AI could wrongly suggest a treatment that doesn’t work for a person’s specific condition.

Stigma and trust

• Some people don’t trust AI and prefer talking to a human.
• Example: Someone might feel more comfortable opening up to a therapist, not a machine, especially when dealing with sensitive topics like depression.

Accessibility

• Not everyone has access to AI tools, especially in poorer areas.
• Example: A person without a smartphone or internet might miss out on helpful mental health apps.

Cost and development

• Developing AI tools can be expensive, and smaller clinics might not afford them.
• Example: A small therapy clinic might not be able to pay for the latest AI tools to help their patients.

While AI can solve a lot of current issues in the field of mental healthcare. But is it the only best solution? We need to think through before we arrive at a final answer. Stakeholders need to check for biases in AI models before using them in situations where people’s lives and well-being are at stake. As we gain a better understanding of using AI, we can make a better and stronger case for using these technologies on a larger scale.