A.I’s Potential in Helping Us to Overcome and Treat Mental Health Illness

The world is stressed. Family violence, isolation and depression had plagued the news in the last few years since Singapore, and many parts of the world went into Lockdown in an effort to curb the COVID-19 virus. 

The novel strain of the Coronavirus, Covid-19, has brought to the table many questions not only around the disease itself but the way humans cope with it. Mental health illness is sometimes an overlooked “virus” that spreads across our society and kills on average eight million people every year, with the median reduction in life expectancy among those with mental illness being 10.1 years (from 1.4 to 32 years), according to the US’ National Institute of Mental Health.

The numbers are staggering in normal circumstances, but Covid-19 has gotten specialists in the four corners of the world worrying about the mental health impact the pandemic and necessary lockdowns will have on people – at the time of writing, more than half of the world’s near 7.8 billion inhabitants were either restricted in their movements or living under total draconian lockdown.

The death of a loved one, isolation, unemployment and loss of income are just some of the side effects the virus is having on millions across the planet. And although the pandemic is expected to be under control worldwide in 18 to 24 months, in the worst-case scenario, the repercussions could last for years.

“Depression is one of the causes, affecting 264 million people worldwide, according to the World Health Organization (WHO). Suicide results in almost 800,000 deaths every year: that’s one every 40 seconds.”

While some may consider the digitization of mental health services impersonal, the inherent anonymity of AI turns out to be a positive in some instances. Patients, who are often embarrassed to reveal problems to a therapist they’ve never met before, let down their guard with AI-powered tools. The lower cost of AI treatments versus seeing a psychiatrist or psychologist is another plus. These advantages help AI tools ferret out the undiagnosed, speed up needed treatment, and improve the odds of positive outcomes.

Like all digitization efforts in health care and other industries, these new tools pose risks, especially to patient privacy. Health care has already become a prime target of hackers as more and more records have been digitized. But hacking claims data is one thing; getting access to each patient’s most intimate details presents a whole new type of risk — particularly when those details are linked to consumer data and social media logins. Providers must design their solutions from the outset to employ mitigation techniques such as storing minimal personally identifiable data, regularly deleting session transcripts following analysis, and encrypting data on the server itself (not just communications).

AI vendors also must deal with the acknowledged limitations of AI, such as a tendency for machine learning to discriminate based on race, gender, or age. 

For instance, if an AI tool that uses speech patterns to detect mental illness is trained using speech samples only from one demographic group, working with patients from outside that group might result in false alerts and incorrect diagnoses. Similarly, a virtual therapist trained primarily on the faces of tech company employees may be less effective reading non-verbal cues from women, people of color, or seniors — few of whom work in tech. To avoid this risk, AI vendors must recognize the tendency and develop AI tools using the same rigorous standards as research clinicians who diligently seek test groups representative of the whole community.

More broadly, AI’s scale can be both a blessing and a curse. With AI, one poor programming choice carries the risk of harming millions of patients. Just as in drug development, we’re going to need careful regulation to make sure that large-scale treatment protocols remain safe and effective.

But as long as appropriate safeguards are in place, there are concrete signs that AI offers a powerful diagnostic and therapeutic tool in the battle against mental illness. Below, we examine four approaches with the greatest promise.

Making humans better. At their most basic level, AI solutions help psychiatrists and other mental health professionals do their jobs better. They collect and analyze reams of data much more quickly than humans could and then suggest effective ways to treat patients.

Ginger. Io virtual mental health services— including video and text-based therapy and coaching sessions — provide a good example. Through analyzing past assessments and real-time data collected using mobile devices, the Ginger.io app can help specialists track patients’ progress, identify times of crisis, and develop individualized care plans. In a year-long survey of Ginger.io users, 72 percent reported clinically significant improvements in symptoms of depression.

“The University of Southern California’s Institute for Creative Technologies has developed a virtual therapist named Ellie that hints at what’s ahead. ”

Anticipating problems. Mental health diagnosis is also being supplemented by machine-learning tools, which automatically expand their capabilities based on experience and new data. One example is Quartet Health, which screens patient medical histories and behavioral patterns to uncover undiagnosed mental health problems. For instance, Quartet can flag possible anxiety based on whether someone has been repeatedly tested for a non-existent cardiac problem.

It also can recommend pre-emptive follow-up in cases where patients may become depressed or anxious after receiving a bad diagnosis or treatment for a major physical illness. Already being adopted by insurance companies and employer medical plans, Quartet has reduced emergency room visits and hospitalizations by 15 to 25% for some of its users.

Dr. Bot. So-called chatbot counseling is another AI tool producing results. Chatbots are computer programs that simulate human conversation, either through text or a voice-enabled AI interface. In mental health, these bots are being pressed into service by employers and health insurers to root out individuals who might be struggling with substance abuse, depression, or anxiety and provide access to convenient and cost-effective care.

Woebot, for example, is a chatbot developed by clinical psychologists at Stanford University in 2017. It treats depression and anxiety using a digital version of the 40-year-old technique of cognitive behavioral therapy – a highly structured talk psychotherapy that seeks to alter a patient’s negative thought patterns in a limited number of sessions.

In a  study university students suffering from depression, those using Woebot experienced close to a 20% improvement in just two weeks, based on PHQ-9 scores — a common measure of depression. One reason for Woebot’s success with the study group was the high level of participant engagement. At a low cost of $39 per month, most were talking to the bot nearly every day — a level of engagement that simply doesn’t occur with in-person counseling.

Today’s mental health AI solutions may be just the beginning. The University of Southern California’s Institute for Creative Technologies has developed a virtual therapist named Ellie that hints at what’s ahead. Ellie is far more than the usual chatbot — she can also detect nonverbal cues and respond accordingly. For instance, she has learned when to nod approvingly or perhaps utter a well-placed “hmmm” to encourage patients to be more forthcoming.

Ellie — an avatar rendered on a 3D Television screen— functions by using different algorithms that determine her questions, motions, and gestures. The program observes 66 points on the patient’s face and notes the patient’s rate of speech and the length of pauses before answering questions. Ellie’s actions, motions, and speech mimic those of a real therapist — but not entirely, which is an advantage with patients who are fearful of therapy.

As with all potential breakthroughs, caveats remain and safeguards must be developed. Yet, there’s no doubt we’re on the cusp of an AI revolution in mental health — one that holds the promise of both better access and better care at a cost that won’t break the bank.