Author: Aziz Khan
Part 1: How will AI positively affect mental health in the future?
What comes to mind with the term “artificial intelligence?” For many of us, this conjures up robots or science fiction movies like Star Wars. However, most of us use artificial intelligence (AI) in our daily life nowadays without even noticing it.
Think of the last time you used a mapping system on your phone or a personal assistant like Alexa. These are both examples of technology that makes use of artificial intelligence. Essentially, AI refers to the concept of computers solving problems on their own.
Artificial intelligence also has the potential to make strides in the healthcare field due to the means to quickly assimilate large quantities of big data, notice trends, and correlations, and analyze data with algorithms in other exciting ways. An intriguing area for growth in healthcare is in the area of AI and mental health.
Part 2: Importance of Mental Health
Mental health and wellness are an important part of overall health and wellbeing. In the United States, mental illness is among the most common illnesses, and the CDC estimates that 50% of the population will experience mental illness at some point in their life. As we move through different seasons in our lives, mental health can also change.
Overall mental health has an impact on how we think, feel, and interact in our lives. Further, it can also affect how we treat ourselves, interact with others, handle stress, and make healthy decisions. Mental health can even affect overall physical health.
For example, depression can increase the risk of metabolic or cardiovascular disease, such as diabetes or heart disease. With such a large percentage of the population affected by mental illness, what can we do to expand the reach of mental health services? AI has the potential to enhance our currently existing mental health resources.
Part 3: Increased Need for Mental Health Services
During the Covid pandemic, there was a marked increase in the amount of mental health support needed versus the number of qualified professionals. For example, rates of anxiety symptom sufferers just before the pandemic in December 2019 were roughly 8% and they jumped to 37% after April 2020. However, the number of available health professionals cannot keep up with this demand.
A recent Harvard University study revealed that only 17% of phone calls to schedule an appointment with a mental health counselor were successful. Waitlists to get in to see a provider are sometimes weeks or months long. Using AI to assess anxiety easily and unobtrusively is extremely valuable to obtain mental health data about the impact of anxiety, particularly when professionals are harder to access. Additionally, for those not able to travel to a clinician for an initial diagnosis, this option could be particularly useful.
Part 4: An AI Psychiatrist?
Is the future an AI psychiatrist? According to NIH estimates, as many as two-thirds of those suffering from depression go undiagnosed. Artificial intelligence may offer a solution, thanks to new voice processing technology. Recent advancements in technology show that AI can detect depression in ways that humans cannot. Artificial intelligence uses data science to extract a high quantity of data from a sample.
For example, advances in audio signal processing can detect changes in health or disease symptoms by analyzing vocal biomarker data from an audio sample, meaning that signs of depression can be detected simply from the data present in the way a person speaks. At this point, a human still needs to be involved in diagnosing depression and other mental illnesses. However, artificial intelligence has a bright future in behavioral and mental health care.
Part 5: Personalized Treatment Using Data Science
Within the field of mental health, there is a broad range of mental health conditions as well as individual personality traits, each responding best to a particular type of therapy, therapist, or medication. For example, a patient may need to try multiple different types of depression medication before finding what will be effective.
Mental health big data and machine learning can help make advancements by monitoring trends and collecting data with machine learning. For example, research using brain imaging and machine learning was able to find correlations between an individual’s neural responses to emotional conflict and the likelihood of benefit from an SSRI. Why not use artificial intelligence and mental health algorithms to help a patient find the right treatment more quickly?
Part 6:Artificial Intelligence Can Triage Patients
Harnessing the power of AI can help us to more quickly identify the best type of health professional for an individual with a specific symptom presentation, thereby helping them 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 with this condition. In that vein, using tools to triage patients for mental health care helps to funnel them to the correct professional much more efficiently. This is another exciting area for growth in the healthcare industry with AI.
Part 7: Expanded Access Thanks to AI
Patients can also benefit from improved access to resources conveniently and at a lower cost than traditional therapy. For example, WoeBot is an emotionally intelligent, AI chatbot that uses mental health algorithms to manage distressing thoughts and feelings. This chatbot uses natural language processing to help monitor moods and create a positive, therapeutic experience for its users.
For patients experiencing anxiety due to stigma or social anxiety, this is a great option to provide additional support, all while offering privacy and anonymity. Additionally, these tools can be especially useful in between sessions with a therapist to offer 24/7 additional support, giving patients life tools to manage their symptoms.
Part 8: Optimizing Mental Health with AI
AI notices trends in data collection through machine learning that can be helpful to spot areas of focus for a therapy session. Making use of data from wearable devices, such as a FitBit to monitor the quality of sleep, can also help to inform optimal areas of focus for treatment.
For example, does a therapy session need to focus on sleep hygiene, emotional wellbeing, or other areas to help optimize sleep for this patient? The clinician can make good choices about areas of improvement for patients based on helpful, objective data.
Part 9: Combining Human Expertise with AI Data Analysis
Furthermore, the use of AI is extremely helpful to sort through vast amounts of big data to spot trends and connections for future advancements in mental health. For example, AI can spot trends in terms of 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.
These are tasks that are best synthesized by AI, while human interactions involving empathy will still be best left to humans. The combination of human expertise, skills, and abilities in addition to artificial intelligence tools, means that the future of mental health is full of possibilities.
Conclusion
In conclusion, combining both healthcare professionals’ expertise with AI tools has exciting future implications for mental health. The majority of the population will be affected by mental health challenges at some point in their lives, and thus improving access to resources is vital to maintaining health, both mentally and physically. With current artificial intelligence technology advancements, AI is poised to fill a gap and increase access conveniently, affordably, and efficiently.
