3 ways AI is already being used in mental health care

Artificial Intelligence (AI) in Healthcare: This advancement has no plans to slow down. Even in the past few years, there has been a significant increase in robotic surgery, AI to predict health diagnosis and treatment plans, and AI data analysis to further detect diseases. But one area of ​​medicine that seems a little more difficult to work with AI is mental health. Psychology and mental health treatment have never been positioned in the black-and-white realm of medicine, but have always encompassed gray areas. Good mental health care relies on human connection, emotion, and empathic understanding… things that robots and AI will never know. But perhaps bridging the gap is necessary, and learning how to take advantage of advances in AI could also be beneficial in the area of ​​”gray space.” Here are three ways AI and augmented reality (AR)/virtual reality (VR) are already being used to treat mental health.

motion sensor

The use of trackers is not new. Fitness trackers have been around for a while and have proven to be great tools to aid physical activity by tracking heart rate, steps, distance, sleep patterns, and more. Something similar is currently being developed to track behavior. Mental health issues such as anxiety and depression have associated behaviors. For example, anxiety has been associated with behaviors such as nail picking, pacing, nail biting, hair pulling, and patting. Furthermore, these tend to be automatic behaviors. We can be working on it for a while before we realize it. A study by Khan et al. (2021) demonstrated how to detect anxious behavior using motion sensors. (2023) discussed how the use of wearables and sensors can help teachers and educators detect and manage students exhibiting anxious behaviors. Personal use of such trackers and motion sensors also has applicability, allowing individuals to become aware of these automatic behaviors sooner and apply appropriate coping skills.

This type of sensor is also beginning to be used in formal clinical settings. A major drawback to the rise of telemedicine and teletherapy is that it puts clinicians at a disadvantage when it comes to body language and nonverbal cues. Clinicians are limited in their ability to see nonverbal cues and anxious behaviors through screens. AI is currently being used to detect these nonverbal cues through video recordings. Therapists can be provided with real-time or “post-session” data to assist their clients. Several students from Samsung Electronics’ Samsung Innovation Campus in Valencia, Spain have come together to develop CoteraplA, an AI program that can assist therapists by “giving them a second set of eyes.” Students said the program provides support to mental health professionals by video recording patients’ verbal and nonverbal expressions and providing practical information in an easy-to-digest format. ing.

Supporting therapist effectiveness during sessions

In graduate school, clinicians are taught a number of effective techniques to use with clients. For example, theoretical frameworks ranging from client-centered humanistic styles, motivational interviewing techniques, cognitive behavioral interventions, and dialectical behavioral approaches are all widely used. The concepts of supervision and “human evaluation” have traditionally been used to assess the effectiveness of clinician performance. Recent advances in linguistic AI programs have enabled more accurate scoring of psychotherapy tactics. Fremotomos et al. (2021) discussed the creation of a BERT (Bidirectional Encoder Representation from Transformers) based model of automatic behavioral scoring for cognitive behavioral therapy. This type of automated behavioral coding and session transcription is extremely useful for training new clinicians as well as maintaining the skills of seasoned clinicians. The goal is to not only train more effective therapists, but also to continue real-time assessment of skills, areas of improvement, and effectiveness across the therapeutic spectrum. Below are some BERT-based models already in use that have high applicability to mental health.

BERTweet: A BERT model based on analysis of twitter data to screen mental health discussions on social media

BioBERT: BERT model trained on biomedical context including clinical records

HealthBERT: A BERT model based on health-related text, such as articles and mental health content on the internet.

ClinicalBERT: BERT models trained on clinical texts, electronic medical records, clinical notes, or mental health diagnosis or treatment

virtual reality and augmented reality

Virtual reality (VR) or augmented reality (AR), a subset of AI, is perhaps the most widely discussed technology these days for mental health treatment. The idea is to create an immersive world in which individuals can address their immediate concerns. Most of the current research on VR concerns the treatment of phobias and the use of exposure therapy. Other areas of focus are 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 responses to real-world environments. They further note that “greater capabilities in experimental manipulation and exposure control have the potential to greatly advance the field of mental health by improving methodological rigor and enabling more accurate and individualized assessments.” “There is.”

Advances in AI and VR are certainly exciting, but each also comes with questions and concerns. For example, discussions about clinician access, training, and the willingness to leverage AI technology for sensitive personal issues such as mental health treatment are valid. We ensure that privacy, ethics and transparency are considered. Moreover, the use of such technology comes at a high cost. Some VR software programs currently available to clinicians sell for thousands of dollars, and this does not include clinician training. Either way, it will be interesting to see how the field of mental health adapts to the inevitable advances in AI and how clinicians and technology can work together to improve mental health treatment.

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