Mental health disorders are a growing global concern that impacts an estimated 792 million people around the world, of which 264 million are cited to have depression and 46 million diagnosed with bipolar disorder. According to recent findings from the National Institute of Mental Health (NIMH), approximately one in five adults in the U.S. experiences some type of mental health disorder.
Artificial intelligence’s (AI) ability to identify patterns, learn from data, and make data-driven predictions is the cause for its increased uptake by researchers and clinicians in life sciences, biotechnology, and mental health. For example, a recent study published in Translational Psychiatry revealed how mental health professionals are successfully using AI machine learning to distinguish between bipolar disorder (BD) and major depressive disorder (MDD).
In fact, AI is already able to predict mental illness using social media data with up to 95 percent accuracy, for issues such as eating disorders, stress, and self harm. With the majority of social media users falling within the 15 to 29 year age range, and the World Health Organization reporting suicide as the second leading cause of death of people in the same age group, the ability to predict and prevent potentially fatal incidents through AI is less of a ‘nice-to-have’ and more of an essential technology for professionals in mental health.
Practical Applications of AI for Mental Health Professionals
Using AI Machine Learning to Diagnose Bipolar Disorders
AI tools like SEDGE can use algorithms to accurately identify patients with bipolar disorder (BD) and can help expedite accurate clinical diagnosis and treatment of BD.
For example, a recent study of over 3,000 participants ranging in age from 18–45 years-old with depressive symptoms was carried out. All participants completed an online mental health questionnaire, over 1,300 submitted blood samples, and nearly 1,000 sat for a telephonic diagnostic interview using the World Health Organization World Mental Health Composite International Diagnostic Interview (WHO WMH-CIDI or CIDI). The data was captured, analyzed and processed using a machine learning algorithm with a decision tree-based method called Extreme Gradient Boosting (XGBoost). The blood samples were analyzed for biomarker levels targeting 203 unique peptides that represent 120 proteins. Subsequently the AI tool was able to distinguish between participants who had BP and those who demonstrated MDD symptoms.
AI for Mental Health and Personalized Care
Advances in natural language processing and the increased prevalence of smartphones have made chatbots the new favorite AI application for mental health care. Chatbots are able to ‘listen’ to a person’s state of mind and feelings by asking questions that mirror what a patient’s conversation with their therapist might look like.
Cognitive Behavior Therapy
AI tools like SEDGE are exceptional at identifying patterns in data. This is a symbiotic relationship with disciplines like cognitive behavior therapy (CBT) which seeks to identify and change negative thought and behavior patterns. Chatbots interacting with patients are able to provide evidence-based recommendations based on data.
Chatbot tools aim to replicate a real-life face-to-face conversation, and the interaction is unique and adapts to the individual’s situation. Chatbot technology is constantly improving to become more human-like and natural.
Reduce Student Stress
Exams often put tremendous pressure on young people, which can have negative health implications such as depression, insomnia, and suicide. When exposed to excessive stress, timely counseling can be imperative to maintaining health. Another novel way chatbots support mental health is through providing tailored suggestions, tools, and coping mechanisms for highly stressed university students over exam times.
Solutions for Social Isolation Among Young People with AI
Young people who struggle with mental wellbeing often have difficulty building close relationships and may experience extreme social isolation. Feeling connected is important for people dealing with mental illness, and social networking is one avenue that solves this. As a result, scientists are investigating how AI’s ability to intuitively predict outcomes (be it social connections, content, products, etc) based on historic behavior can play a role in making people feel more connected and less isolated.
The Benefits of AI for Mental Health Professionals
Freedom of expression - Many patients may find it easier to disclose potentially embarrassing information with a chatbot or virtual therapist. There is no shame or judgement with AI, only tailored, personalized responses.
Verify diagnoses - AI tools not only detect words but are able to process nonverbal cues, such as facial expression, gestures, and posture. This visual data is important in therapy but is subtle and may be overlooked. AI tools can gather and analyze multisensory information and help assess a patient and confirm or prescribe a diagnosis.
Affordable solutions - Not only is AI technology becoming more affordable to the mental health practitioner, thanks to cloud and subscription-based tools, the solution is also always available to the patient, and can offer a much higher frequency of therapeutic interactions at a more affordable rate, making essential mental health services available to a wider demographic.
Technology that supports you - While technology is advancing at a rapid rate, there are some tasks that are, and will always be, human. However, AI technology helps practitioners by working in conjunction with their daily tasks, to streamline operations and improve efficacy.
Incorporate the Personalized Power of AI in Your Practice
AI tools like SEDGE are able to quickly and accurately process volumes of data from many sources to give mental health professionals the unbiased insights they need to efficiently diagnose complex, nuanced mental health conditions.
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