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There are a plethora of AI initiatives in progress across thehealthcareindustry. From drug discovery to thermal scans, AI has transformed this sector significantly over the past decade. While we know AIs contributions to physical healthcare, AI is also easing mental health concerns too. According to the Substance Abuse and Mental Health Service Administration (SAMHSA)s 2016 reporton drug use and health, only 63% of adults identified as having had at least onemajor depressive episodereported receiving any kind of treatment.

In the US alone, one in five adultssuffersfrom a form of mental illness. Suicide rates are at an all-time high, 115 peopledie daily from substance abuse, and one in eight Americans over 12 years oldtake an antidepressantevery day. The economic burden of depression alone isestimatedto be a minimum of US$210 billion annually, which includes costs due to increased absenteeism and reduced productivity in the workplace. The situation is grim in other regions as well. For instance, in Europe, 83 million people are struggling with mental health. Other than this, people also have to endure stigma, lack of mental health professionals and high costs of counselling sessions too.

Currently, during the world pandemic crisis, even the COVID-19 has been tangibly affecting millions of people in terms of their mental peace. Rates of depression and panic attacks are much higher than normal.Isolation due to social distancing has triggered sleep deprivation, social anxiety, and reduced happiness while also causing people to worry about their jobs. As per a report, 70% of people have had more stress and anxiety at work this year than any other previous year. This increased stress and anxiety have negatively impacted the mental health of 78% of the global workforce, causing more stress (38%), a lack of work-life balance (35%), burnout (25%), depression due to lack of socialization (25%), and loneliness (14%). A recent report by Deloittesuggests COVID-19s impact on mental health could last for years

While, AI is proving to be an effective way for clinicians to both make the best of the time they do have with patients, and bridge any gaps in access, it has also helped in early prediction and diagnosis of the diseases too. So, incorporating AI to address mental health issues can achieve similar promising results. For starters, this can be done by employing AI into digital interventions, like web andsmartphoneapps, to enhance user experience and optimize personalized mental health care. One can analyze modern streams of abundant data as a means to develop prediction or detection models for mental health conditions. E.g. Quartet Health, has reduced hospitalization of patients by 15-25% by screening patient medical histories and behavioral patterns to uncover undiagnosed mental health problems.

Vanderbilt University Medical Center in Nashville has created a machine learning algorithm that uses hospital admissions data, including age, gender, zip code, medication, and diagnostic history, to predict the likelihood of any given individual taking their own life.Scientists are experimenting with linear classifiers of Natural Language Processing (NLP) to risk assessment in possible PTSD cases.

Also, while AI would not replace existing therapists and psychiatrists, it surely provides a medium where people can talk about their struggles without the fear of being judges or facing stigma. Further, AIcan help doctors and therapists increase emotional awareness for their patients, such as in expressing empathy. Research has also proved that AI helped employees improvetheir mental health at work.

Next, we have emotional AI-powered chatbots (like Wysa, Woebot) that can provide unprecedented accessibility by being available 24/7 at little to no cost. These apps collect data that allow them to create a level of therapeutic rapport with users and offer relevant responses. Mood tracking apps like Woebot, which is created by a team of Stanford psychologists and AI experts, uses brief daily chat conversations, mood tracking, curated videos, and word games to help people manage mental health. This is faster and comfortable than traditional practice in mental health where professionals rely on the individual to observe and self-report indicative changes. E.g. IBMs Computational Psychiatry and Neuroimaging group, alongside several universities, have built a model using NLP to predict the onset of psychosis in patients. This model can detect differences in speech patterns between high-risk patients who develop psychosis and those who did not. At present, a team of scientists at Dublin, Ireland-based startup Behavidence is currently preparing to launch an effective digital phenotyping solution that can provide an accurate psychiatric diagnosis for ADHD.

Mental health will likely remain a major challenge in todays world due to various reasons. Although AI for mental health still needs to deal with many complexities, its applications and tools are doing an appreciable job in alleviating this issue for many. Soon it will be well equipped to mitigate and manage the stress, depression and trauma, things which are living hell for many.

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Easing the lives of Mental Health Patients via Artificial Intelligence - Analytics Insight