Med Tech

Role of AI in Mental Healthcare Tech

The COVID-19 pandemic greatly spurred fear, worry and uncertainty causing many to experience mental health issues such as depression and anxiety. Adjusting to work from home conditions and lack of social exposure only exacerbated the situation thus leading to a sharp rise in various mental health problems.

Due to the lockdowns, it was hard for people to access mental healthcare professionals. Telehealth and telemedicine platforms came to the rescue in this aspect and mental health is one area of healthcare that can be delivered via these platforms without losing its essence. Despite the lack of face to face interactions with mental healthcare professionals, it was easier to access one because of the availability of technological resources. 

Across the mental health industry, companies are rapidly building solutions for monitoring and treating mental-health issues that rely on just a phone or a wearable device via AI.  One of the chief benefits of using AI in clinical care is the technology’s ability to obtain insights from massive amounts of data. AI systems could help providers go through these data resources and collect clinically relevant targets that will improve patient care. AI can also enable providers to tackle these mental health issues in a targeted way (personalised care). 

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Mental health companies are also adopting “affective computing” to detect and interpret human emotions. Affective computing, also known as emotion AI, is a subfield of computer science that originated in the 1990s. Rosalind Picard, one of its pioneers, defined affective computing as “computing that relates to, arises from, or deliberately influences emotions.” 

Affective computing involves creating technology that can recognize, express, and adapt to human emotions. Various sensors, voice and sentiment analysis programs, computer vision, and ML techniques capture and analyze physical cues, written text, and/or physiological signals. These tools are then used to detect emotional changes.

Mental Healthcare startups that are implementing AI

Start-ups and corporations are applying this field of computer science to build technology that can predict and model human emotions for clinical therapies. One such startup, Companion Mx, is a phone application that analyses users’ voices to detect signs of anxiety. 

Sentio Solutions’ MyFeelCo combines physiological signals and automated interventions to help consumers manage their stress and anxiety. They use a sensory wristband that monitors sweat, skin temperature and blood flow, and, through a connected app, asks users to select how the users are feeling from a series of labels, such as “distressed” or “content.” 

Other sensors include the Muse EEG-powered headband that guides users toward mindful meditation by providing live feedback on brain activity, and the Apollo Neuro ankle band that monitors users’ heart rate variability to emit vibrations to reduce stress. 

While wearable technologies are quite expensive for the average user, therapy is now available in the form of mobile applications. App-based conversational agents, such as Woebot, uses emotion artificial intelligence to replicate the principles of cognitive behavioral therapy (CBT), monitor the user’s state of mind, and accordingly delivers advice regarding sleep, worry, and stress. 

Sentiment analysis combines sophisticated natural language processing (NLP) and machine learning techniques to determine the emotion expressed by the user. Ellie, a virtual avatar therapist developed by the University of Southern California, can pick up on nonverbal cues and guide the conversation accordingly, such as by displaying an affirmative nod or a well-placed “hmmm.” 

Wysa, an Indian Mental Health App, takes the help of AI-based emotionally intelligent bots and uses evidence-based CBT techniques and dialectical behaviour therapy to improve mental resilience and the user can remain anonymous while accessing Wysa. However, it does not provide a diagnosis or cure for mental disorders. 

Founded at the MIT Media Lab, Ginger integrates human care with data science and augmented intelligence. Its on-demand platform for mental-health care solutions via a smartphone makes use of health coaches, therapists, and psychiatrists who collaborate to provide personalised attention to the user.

Replika, founded by Eugenia Kuyda in 2017, comes with the concept of personalised AI through which one can express themselves. With the help of the AI companion, the user can express their feelings, issues, and share other intimate details. Some users have described Replika as a non-judgmental support system acting more like a confidant as the AI companion provides a listening ear to the user.

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 order for AI to take a more central role in mental healthcare, investigators and startups have to refine research and analysis in this area and test out the effectiveness of these tools.

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