3 Ways AI is Being Used in Mental Health Care

6 min read

Education & Career Trends: February 17

Curated by the Knowledge Team of ICS Career GPS

One area of healthcare that has seemed a bit more challenging for AI to interface with has been mental health.

Artificial intelligence (AI) in healthcare: an advancement that is here with no plans of slowing down. Even in the last several years, we have seen a massive surge in robotic surgeries, AI-predicted health assessments and treatment plans, and further detection of diseases through AI data analysis. However, one area of healthcare that has seemed a bit more challenging for AI to interface with has been mental health.

Psychology and mental health treatment have never been located in the black-or-white sphere of healthcare, but have always encompassed a gray space. A great piece of mental health care relies on human connection, emotions, and empathic understanding… things that robots and AI aren’t known for.

But perhaps bridging the gap is necessary, and learning ways of utilising AI advancements can also be beneficial in a “grey space” field.

Here are three ways that AI and augmented reality (AR)/virtual reality (VR) are already being used to treat mental health.

1. Motion Sensors

The use of trackers is not novel. Fitness trackers have been around for some time, and have proven to be a great tool in aiding physical activity by tracking heart rate, steps, distance, sleep patterns, etc. Something similar is now being developed to track behaviours.

Mental health struggles, such as anxiety or depression, have associated behaviours. Anxiety, for example, has been associated with behaviours such as picking, pacing, nail-biting, hair pulling, or tapping. Furthermore, these tend to be automatic behaviours; things we likely engage in for some time before becoming aware of them. Research by Khan et al. (2021) demonstrated how motion sensors can be used to detect anxious behaviours, and Mastrothanasis et al. (2023) discussed how using wearables or sensors can help teachers and educators detect and manage students exhibiting anxious behaviours. Personal use of such trackers and motion sensors also has applicability, allowing individuals to become aware of these automatic behaviours sooner and apply appropriate coping skills.

These types of sensors are also beginning to be used in formal clinical settings. A major drawback of the increase of telehealth and tele-therapy has been the disadvantages that clinicians have concerning body language and nonverbal cues. Through a screen, a clinician is limited in seeing nonverbal cues and anxious behaviours. AI is now being used to detect these nonverbal cues through video recording. Therapists can be given real-time or “after-session” data to help assist their clients. Several students from the Samsung Electronics’ Samsung Innovation Campus in Valencia, Spain came together to develop CoteraplA, an AI program that can help therapists by “giving them a second set of eyes.” The students state the program gives mental health professionals a helping hand by recording patients’ verbal and non-verbal expressions via video, and then providing actionable information in an easy-to-digest form.

2. Assisting Therapist Efficacy in Session

In graduate studies, clinicians are taught numerous effective techniques to employ with clients. For example, theoretical frameworks ranging from client-centred humanistic styles, motivational interviewing techniques, cognitive-behavioural interventions, and dialectical-behavioural approaches are all widely used. Supervision and “human-rating” constructs have traditionally been used to assess the efficacy of clinician ability. Recent advances in linguistic AI programs have allowed more accurate scoring of psychotherapy tactics.

Flemotomos et al. (2021) discussed creating a BERT (bidirectional encoder representations from transformers)-based model of automatic behavioural scoring for cognitive-behavioural therapy. This type of automatic behavioural coding and transcription of sessions can be extremely useful in training new clinicians and maintaining seasoned clinicians’ skills. The goal is not only to train more effective therapists but to continue real-time assessment of skills, areas of improvement, and efficacy across the scope of therapy. The following are several BERT-based models already being used that have strong applicability for mental health:

BERTweet: BERT model based on analysing Twitter data to screen mental health discussions on social media

BioBERT: BERT model trained on biomedical contexts including clinical notes

HealthBERT: BERT model based on health-related text such as articles or mental health content on the internet

ClinicalBERT: BERT model that is trained on clinical text, electronic medical records, clinical notes, or mental health diagnosis or treatment

3. Virtual and Augmented Reality

A subset of AI, virtual reality (VR) or augmented reality (AR), is perhaps the most widely discussed technology in recent times concerning mental health treatment. The idea is to create an immersive world where the individual can work through immediate concerns. The bulk of current research regarding VR has been in the treatment of phobias or utilising exposure therapy.

Other areas of focus have been in the treatment of schizophrenia, social anxiety, eating disorders, and addiction. According to Bell et al. (2020), VR has been shown to elicit similar physiological and psychological reactions to real-world environments. Further, they state that “superior capabilities for experimental manipulation and controlled exposure could significantly advance the field of mental health by improving methodological rigour as well as enabling more accurate and individualised assessment.”

The advances in AI and VR are certainly exciting but come with their questions and concerns. For instance, discussions about clinician access, training, and willingness to utilise AI technologies in sensitive personal matters such as mental health treatment are valid.

Considerations of privacy, ethics, and transparency are warranted. In addition, the cost of utilising such technologies comes with a steep price tag. Several VR software programs that are currently available to clinicians are selling for thousands of dollars, which does not include clinician training. Either way, it will be interesting to see how the field of mental health adapts to the inevitable advancements in AI and how clinicians and technology can work together to improve mental health treatment.

Have you checked out yesterday’s blog yet

6 Vital Ways To Deal With Stress

(Disclaimer: The opinions expressed in the article mentioned above are those of the author(s). They do not purport to reflect the opinions or views of ICS Career GPS or its staff.)

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