How AI Can Help Mental Health

How AI Can Help Mental Health

How AI Can Help Mental Health

In today’s world, the need for mental health resources is urgent. Globally, in addition to great need for mental health services, there is simultaneously a shortage of mental health providers. As we know, AI refers to the concept of computers solving problems on their own. What better way to help us solve this problem and create more resources than with AI? In fact, artificial intelligence is making advancements in many exciting areas of mental health.

Why Mental Health Is So Important

Overall health and well being is heavily influenced by mental health. According to recent UN data, nearly one billion people suffer from mental illness. Mental health status is also fluid over the course of life. How we feel also affects how we interact with others, treat ourselves, make healthful decisions, and handle stress. Mental health status also has implications for physical health. For example, depression can increase risk of diabetes, immune system function, stroke, and heart disease.

Could there one day be an AI psychiatrist? The NIH estimates that as many as two thirds of those suffering from depression go undiagnosed. A new voice processing technology powered by AI may offer a solution. In fact, recent advancements in technology show that AI is able to detect depression twice as reliably as humans can. AI extracts data from a sample and subsequently evaluates a variety of metrics within the sample. For example, the technology can detect signs of depression by analyzing vocal biomarker data, such as pitch or rhythm of speech, from an audio sample. Currently, we still need a human to diagnose depression and other mental illnesses. However, there is huge potential for AI to shape behavioral and mental health care.

AI can help us to identify the best health professional for specific symptom presentation, thereby helping patients to get to the right professional more efficiently. For example, Mayo Clinic has begun making use of AI to triage complex illnesses, such as dizziness. There are many reasons a patient may feel dizzy, one reason being anxiety. On average, a patient visits seven different doctors or professionals before being properly treated and diagnosed for this condition. Using tools to triage patients for mental health care can reduce the number of appointments to finding the correct professional.

Each individual has their own unique needs, and different types of mental health conditions as well as individual personality traits will respond best to a particular type of therapy, therapist, or medication. For example, currently a patient may need to trial several different types of medication before finding the most effective depression medication for them. Machine learning can help make advancements by monitoring trends and collecting data to streamline this process. For example, research using brain imaging and machine learning was able to find correlations between an individual’s neural responses to emotional conflict and likelihood of benefit from an SSRI. Instead of using trial and error, why not use artificial intelligence and mental health algorithms to find the right treatment more quickly?

Some additional benefits to using AI include improved access to resources conveniently and at lower cost than traditional therapy. For example, an emotionally intelligent AI chatbot, called WoeBot, uses mental health algorithms to manage distressing thoughts and feelings. This chatbot monitors moods and creates a positive, therapeutic experience for its users. For those experiencing anxiety due to social anxiety or stigma, this tool is a great resource, offering additional support as well as privacy and anonymity. Additionally, these tools offer additional, around-the-clock support in between therapist sessions, giving patients life tools to manage their symptoms.

AI data collection can offer insights for therapy sessions. For example, making use of wearable devices such as a FitBit to monitor quality of sleep and other metrics. For example, the therapist receives objective data and can spot trends to focus on, such as sleep hygiene or emotional wellbeing. The clinician is able to make good choices about areas of improvement for patients based on helpful, objective data.

Using AI to analyze vast amounts of big data and spot trends will help us to make connections and advancements more quickly than humans alone could do. For example, AI can spot trends within complex data such as family history, effective past treatments, patient behavior, and other factors to provide a more precise diagnosis and help both patient and clinician to achieve the best outcome. While these tasks are best synthesized by AI, human interactions involving empathy will still be best left to humans. The winning combination of human expertise, skills, and abilities in addition to artificial intelligence tools, means that the future of mental health is full of possibilities.

The Future of Mental Health with AI

In conclusion, combining both healthcare professionals’ expertise with AI tools has exciting future implications for mental health. Mental health challenges currently affect 1 in 7 people globally, and thus improving access to resources and advancements in treatment are vital to maintaining health, both mentally and physically. AI is poised to fill a gap in the area of mental health and increase access conveniently, cost effectively, and efficiently.

Dr. Amy Sarow obtained her Bachelor’s degree from Central Michigan University and completed her AuD at the University of Iowa. She spent her 4th year residency at the Mayo Clinic of Health Sciences in Jacksonville, Florida. She currently resides in Michigan.

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