What Role Could Artificial Intelligence Play in Mental Healthcare?

AI as a means to personalize care, increase access, and provide support

If there is one chief benefit of using AI in clinical care, it’s the technology’s ability to obtain insights from massive amounts of data. 

This advantage still stands when AI is applied to the mental health space, noted John Torous, MD, director of the digital psychiatry division at Beth Israel Deaconess Medical Center.  

“In mental health, we've had several innovations,” he said.

“First, we had the genetic revolution where we had all this new genetic data and it unlocked the key to understanding mental health. We've also had neuroimaging to help us understand how the brain works, as well as smartphone and sensor data. Intuitively, we know that these tools contain important information that we could use to personalize care for patients, but it's been very hard to unlock that data for clinical insights on a routine basis.”

AI systems could help providers go through these data resources and collect clinically actionable targets that will improve patient care.

“By doing that, we may be able to offer more personalized and preventive care. And hopefully, we can approach these mental illnesses in a more targeted way,” said Torous.

For Prochaska and her team, delivering care that is personalized to the user was top of mind when developing their AI-powered chatbot.

“Woebot is based on cognitive behavioral therapy principles. It has an empathy component that's tailored to the messages that the individual sends,” said Prochaska. 

“It's designed to target cravings and urges and to help the individual build self-awareness in terms of their patterns of thinking, mood-related thinking, anxiety, depression, as well as the urge and craving to use.”

Another advantage of AI-driven tools? Increased access. 

“Chatbots can interact with an individual in real time. They're available 24/7, at no cost, and they reduce stigma in terms of accessing treatment. Whether these tools are used as stand-alone treatment agents or as an adjunct to more traditional counseling, chatbots provide added therapeutic content,” Prochaska said.

Advanced analytics technologies also have the potential to remove access challenges for marginalized groups, Gaunt pointed out.

“AI can increase equity and access to mental health services, especially for LGBTQ youth who exist at an intersection of identities – because we know that LGBTQ youth already face injustices and discrimination in their everyday lives. This is only compounded when you layer on race, ethnicity, socioeconomic status, gender identity, and so on,” she said.

“When I think about how AI can evolve to support people who represent a variety of lived experiences, I think about eliminating barriers to convenience, or access, or privacy. So, making services available 24/7 on demand in different platforms, and also giving people the space to have sensitive conversations – even more so for ones that they might not feel comfortable having out loud.”

While the use of AI comes with several potential benefits, it will be critical to maintain care approaches that are focused on the patient-provider relationship. 

“There will always be a need for human-to-human connection. AI's role in this space shouldn’t be to replace humans, it should be to support them,” said Gaunt. 

“At Trevor, we are not setting out to design an AI system that will take place of a counselor, or that will directly interact with a person who might be in crisis. Instead, we're designing AI to work in partnership with counselors.”

According to Imel, this is the most feasible application of AI in the mental healthcare field.

“The role of AI in mental healthcare that I'm most excited about, and that has the most potential to make an impact now, is supporting human therapists,” he said. 

“Right now, therapists are mostly on their own in the room with the clients and even afterwards. After you're trained as a therapist, you don't get supervised much anymore in most places. You're using your judgment and training to try and make sure you're doing the right things. There are AI tools now that can listen into these sessions and give therapists some pretty useful feedback –even things like how much you talked and the types of basic interventions you used.”

Ensuring AI helps, not harms

For all of the good AI could do, there are a number of significant barriers to using the technology in mental healthcare. 

With smartphone apps and chatbots, patient engagement is a key factor in determining the success of the technology. 

“One challenge for mobile health applications is making sure people come back and interact with it on a regular basis,” said Prochaska. 

“Typically, the more the individual uses it, the greater the benefit they'll get from it. Anything you can do to boost engagement should help with your outcomes in terms of accuracy and effectiveness.”

Additionally, developers and researchers need to make sure they equip these tools with appropriate protections for high-risk patients.

“Woebot does have some safety features built into it, like language detection and rules for risk management. It does state explicitly that it is not a suicide prevention tool,” said Prochaska.  

“And then for our study, because it was our first evaluation of the tool, we were thoughtful in terms of our inclusion and exclusion criteria. For example, if we felt that somebody was at risk for overdose, we didn't include them in this first evaluation.”

The data used to train AI models is a crucial aspect of their clinical utility – and data quality can be particularly challenging when it comes to mental healthcare.

“AI is only as good as the data it's trained on and the people that are using it. Each of those are important considerations,” said Torous.

“In terms of the data it's trained on, we have to wonder what the gold standard is that we're using. It's especially hard to have a gold standard in mental health because we know that the current clinical definitions we use are not ideal. We have to take a very careful approach to how we're training these algorithms, and it's not going to be as simple as scoring people’s symptoms.” 

Torous also cited the importance of partnerships between industry and academia to develop AI systems that will effectively address the complexity of mental illnesses.

“If we're just solving very easy problems, it doesn't advance care. If we have an algorithm that can tell whether someone has schizophrenia or not, it's useful, but it's not really going to advance the field. That's a very small improvement,” he said. 

“We also need to have diverse samples on which to train these models, because if we only take one region, one clinic, or one population, these algorithms are going to have very limited utility. These tools have to be built from the ground up with a very diverse approach – you have to work with the patients, as well as consider input from clinicians, data scientists, and regulators.”

Imel stressed the significance of collaborative development as well. 

“We have to think about how to build partnerships with patients, stakeholders, and therapists around the data that's going to be necessary to build these tools,” he stated.

“We have to start thinking about how get buy-in from patients as well as providers. The chat platform that a patient is using is capturing their data, and the people behind that platform are trying to use your data to improve care. Similarly, providers are often not super excited about being recorded, so how do we get buy-in from them? Because if we don't, it's not going to work.”

Where AI and mental health will meet in the future

While the future application of AI in any area of healthcare is still unclear, this reality may be especially true in the mental health space.

The use of AI in therapy is dependent on several factors, and even if the industry is able to overcome some major challenges, the technology isn’t likely to appear in front-facing mental healthcare delivery.

“In the future, AI may be helping us on research – sorting through data to find new patterns that may help us understand how mental health illnesses develop, how they spread, and how we can prevent them,” Torous said.

In order for AI to take on a more central role, investigators will need to refine research and analysis in this area, Torous added.

“We know that people are interested in chatbots, and we know that patients are willing to try them. But in 2021, we really want to be answering questions like ‘How well does it work?’ Or, ‘Can we conduct higher quality studies?’ We've seen the interest, and hopefully that catalyzes more development,” he said.

Prochaska noted that she and her team plan to further test the Woebot tool to evaluate its effectiveness. 

“This is the first study in a series of three that we have planned. We see this as building a program of research and building the evidence basis for the program, and then we can make improvements in the program as we go,” she said.

One of the most important things to remember is that the development of these tools should be an ongoing undertaking – and not a one-and-done endeavor, Gaunt stated.

“The application of AI is a process that should evolve as we do,” she concluded. 

“Just because we’re doing one thing today doesn't mean that's how it always can and should be done in the future. Considering how the world around us can change – not just by the day or the hour anymore, but in minutes or seconds – that's important. There's always going to be a new model, a new technique, or knowledge that we acquire.”