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How AI Is Combating Mental Health Problems

In the United States, one-in-five adults currently suffer from some form of mental illness. Globally, 15.5% is affected by it. We’re on the cusp of a global mental health crisis and we’re struggling to keep up with it. In fact, 40% of Americans reside in areas where there is a lack of mental health professionals […]

In the United States, one-in-five adults currently suffer from some form of mental illness. Globally, 15.5% is affected by it. We’re on the cusp of a global mental health crisis and we’re struggling to keep up with it. In fact, 40% of Americans reside in areas where there is a lack of mental health professionals on call. Some estimates even suggest that mental health will cost the global economy $16 trillion between 2020 and 2030.

But that’s where progress in technology can present interesting opportunities to help diagnose, treat and educate on mental health. Through automation, machine learning and artificial intelligence, companies are finding ways to detect early patterns of mental illness, give safer and more confidential ways for people to identify symptoms and provide safe, accessible opportunities for patients to pursue treatment. Here are five different companies at the forefront of technology combatting mental health. 

Lyra: Connecting patients to health professionals

Californian startup Lyra uses big-data technology to help companies connect their employees to mental health providers using personalised algorithms based on machine learning. With over $83 million in funding, it helps provide a digital, scalable solution to healthcare accessibility. The company is positioned towards empowering employers to help their employees in a confidential and safe environment. A lot of patients don’t know when to start when treating mental illnesses, and that’s where Lyra steps in. Users can input their symptoms which are then matched to recommended psychiatrists, therapies or coaching programs specifically tailored to that user.

Neurotrack: Combatting memory loss using AI

Another Californian startup, Neurotrack utilizes AI technology to diagnose and prevent memory loss induced by diseases like Alzheimer’s. Having recently gone through a Series C, bringing their total funding to over $50 million, Neurotrack is one of the leading companies tackling memory loss. Users take part in an Imprint Memory Assessment – a five-minute test where patients’ eye movements are tracked while looking at pictures, to crate ana overall picture of their memory health. So far, clinical studies have shown an overall improvement of 25 per cent using Neurotrack, along with a 150 per cent improvement in processing new information.

JJAIBOT: Chatbots, AI and Depression 

JJAIBOT uses artificial intelligence to power a chat-bot designed at detecting signs of depression and anxiety in the speech patterns of children. Using machine learning based on cognitive behaviour, JJAIBOT builds emotional profiles of its users, recommending and curating personalised activities such as meditation and breathing techniques. With over one-million followers on Instagram, the bot is successfully engaging and educating young people on mental health, while bringing more awareness and acceptance to it.

Moodpath: Identifying symptoms of mental health using AI

German startup Moodpath helps people recognise symptoms of depression by answering daily questions over a two-week period. It creates personal emotional health reports based on the users psychological, physical and emotional health. This can then be used as a starting point when approaching therapists or other mental health professionals. By utilizing artificial intelligence, the company provides a scalable and easy way for people to begin the conversation and identify potential health issues. As of now, more than one-million users are registered on Moodpath. 

IBM: Using speech recognition to identify mental health

Technology leader IBM is using advanced AI to understand transcripts and audio input from psychiatric interviews to identify patterns of speech that can accurately predict psychosis, schizophrenia, mania and depression. By recognising patterns it can construct profiles of interviewees that are designed to help clinicians with the diagnosis. Impressively, it only takes about 300 words to predict the probability of psychosis in a given user. 

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