There’s a lot of hype around the potential for AI in the mental health space.

Companies are eager to get in on the AI hype, but it’s important to be realistic about what AI can achieve. The promise of “curing” mental illness with technology is far off, especially given that many companies are using old data and simple algorithms that don’t consider social context or human behavior. Now, AI is becoming so advanced that it can factor these into the algorithm and produce a better result. Not cure, but help in the process of getting a diagnosis so it can be treated properly and professionally.

The potential for bias in this space is high—if a team doesn’t have diverse perspectives at the table, they may not think of everything they should when building their product. It’s also possible that an algorithm will learn from biased data, or even reinforce those biases (as we’ve seen with facial recognition software). Finally, even if an algorithm works perfectly on its own and doesn’t reinforce any biases during development, there’s no guarantee it will work as intended when used by humans who cannot see the code behind it. That is why it is important to have someone on your team when implementing this type of technological advancement, that can understand the underlying code to test and fix things. Either a permanent staff member (best) or at least someone to be there for the first few months of use in order to help get the bugs out of the system so it is suitable to be used in such a private and professional setting.

AI research has yet to be validated by rigorous clinical studies.

AI research has yet to be validated by rigorous clinical studies. Some of the most promising applications for mental health are still in the research stage, and many of them have yet to be rigorously tested. It is important, as it is with all things based on science, to be accurate enough to be peer-evaluated and tested to the point of perfection (or close to it.) You can’t just slap something together and sell it as some have. You must test, test again, and retest these types of AI software in order to get the results you need as close as possible. Otherwise, you’re wasting patients’ time and money.

Ai-based treatments are likely to be used primarily as an adjunct or complement to existing therapies and medications, rather than instead of them. This approach is consistent with current treatment guidelines for mental illness published by the American Psychiatric Association (APA), which recommend psychosocial interventions like cognitive behavioral therapy (CBT) as first-line treatments for many psychiatric conditions.

Algorithms are only as good as the data they’re based on.

One of the biggest problems with algorithms is that they’re only as good as the data they’re based on. They need A LOT of data. 

For example, if someone has a mental illness and they go to see a doctor, a psychologist, or a psychiatrist, their diagnosis may be wrong because there’s no way for them to know what kind of symptoms are normal for that person. This can lead them to take medication when it might not be necessary or get into therapy when it might not be effective for that particular problem. This happens a lot already. People describe something that may be normal for that person, but abnormal to ‘the norm’ and therefore medication is prescribed. Always be as honest as you can with anyone prescribing you medication. The adverse side-effects of some medications and taking medication that can be potentially addicting can indeed hurt more than help.

It’s also important to keep in mind that an algorithm isn’t just going to tell you what diagnosis you have; it’ll also give you advice about how best to manage your condition from day-to-day life: how much exercise or sleep should I get? What should I eat? Should I talk about my feelings with family members or friends? Should I go outside more often? And so on…there are many things to consider when implementing AI into the mental health space.

Even limited data can help provide better treatment access though.

In our modern world, we have access to more information than ever before. But it’s not just the volume of information that matters—it’s also what we do with it and who is using it.

In a study published in the Journal of Anxiety Disorders, researchers found that “the majority (62%) of individuals who were currently seeking traditional mental health treatment did not seek help from a clinician until after experiencing their first episode”—i.e., after they’d reached their “rock bottom” and had nowhere else to turn. The researchers suggested that by using technology such as online therapy platforms and messaging apps for self-monitoring, people could be better positioned to find support earlier on in their struggles with mental health issues. These are areas in which AI can really help give access to those without and refer them to a professional mental health care provider.

What does this mean for how AI will improve mental health? In short: even limited data can help provide better treatment access; even small gains will make a big difference in reducing the stigma surrounding psychological conditions by normalizing them through increased awareness and empathy; even small gains will help those who need it most get the care they deserve—and don’t yet know where or how they can find it. AI can help there.

AI could help with person-to-person therapy.

AI could help with person-to-person therapy.

Nowadays, the word “therapy” is commonly associated with “online.” As are a lot of things in the world of 2022. There are many reasons for this: the recent pandemic that we’re still not completely out of yet, sessions can be cheaper and more convenient because you can choose your own schedule, you don’t have to drive anywhere or take time off work, and it’s not as uncomfortable as going into a therapist’s office for the first time. There are also some drawbacks to online therapy—for example, if you’re dealing with something personal or traumatic in nature (such as abuse in all of its various forms), it may be hard to open up about these issues over text message or video chat. It seems impersonal. However, AI could help therapists be more effective by providing them with insights from thousands of clients at once that might otherwise take hundreds of years (or never happen) if they were doing research on their own.

AI might help do some of the heavy lifting (scheduling calls/texts/video chats).

As technology becomes more integrated into our lives every day, we will probably see new ways of making use of it in mental health care settings too! For example: could an AI assistant automatically schedule all your appointments? You know, that pesky time after your visit when you just want to leave and process what just happened but instead you’re stuck at the front desk in front of the next patients? This would save face, time, and energy on both ends—yours when making appointments through your phone app vs having someone else do it manually; and theirs when entering data into their system instead of using software like Google Calendar which already exists today!

There is great potential for AI in mental health, but it’s also early days.

AI is a promising tool for improving mental-health care, and there are many ways it can be used. For example, algorithms can be trained to pick up on subtle patterns and cues in phone calls with people who have depression. This could help identify at-risk individuals more quickly. Additionally, AI can also help with suicide prevention by offering support via text message or even phone call to those in crisis or who are feeling suicidal instead of having to wait. However, if you’re feeling suicidal or have deep depression and are feeling like you may harm yourself or others, the hotline for that is listed here for your convenience. You can also visit our mental health hotline page if you’re experiencing another mental health crisis or issue.

National Suicide Prevention Lifeline:

Some other examples of how artificial intelligence could be used include early detection of mental health problems and diagnosis by scanning social media posts or emails for signs of distress (for example references to sadness or suicide). Ultimately, there is unlimited potential for AI in the mental health sphere. However, it needs time to develop, data, and the right people to use it and test it in order for it to work for the betterment of humanity.