There are recent advances in artificial intelligence that facilitate the use of AI in mental healthcare.
With the advent of digital approaches to mental health, modern artificial intelligence is being used in the development of prediction, detection, and treatment solutions for mental health care. Although there has been considerable progress in digital health, the adoption of AI in mental health is relatively evolving. Unlike medical areas such as radiology or pathology where AI can outperform doctors, mental healthcare delivery is thought of by many as an exclusively human field where emotional intelligence is essential. Most mental health practitioners doubt that artificial intelligence solutions for mental health will ever be able to provide emphatic care. But there are recent advances in artificial intelligence which incorporate the benefits of AI in mental healthcare. On that note, this article lists the top use of AI in mental healthcare.
Advances in natural language processing and the popularity of smartphones have made chatbots the new starlets of AI for mental health care. Chatbots are constantly improving to become more human-like and natural. They also offer different language options. For example, Emma speaks Dutch and is a bot designed to help with mild anxiety, while Karim speaks Arabic and has been assisting Syrian refugees struggling to cope after fleeing the atrocities of war.
The field of affective computing, also more commonly referred to as emotion AI. Affective computing is defined as “computing that relates to, arises from, or deliberately influences emotions.” It involves the creation of technology that is said to recognize, express, and adapt to human emotions. Effective computer scientists rely on sensors, voice and sentiment analysis programs, computer vision, and ML techniques to capture and analyze physical cues, written text, and/or physiological signals. These tools are then used to detect emotional changes.
Conducting Therapy Sessions
This category is largely represented by keyword-triggered and NLP chatbots that help patients evaluate the progression and severity of a mental illness and cope with its symptoms either on their own or with the help of a certified psychiatrist waiting on the other end of the virtual line. An example of this would be Ellie, a digital avatar that was initially designed to help war veterans struggling with depression and PTSD. The AI therapist not only understands words but can also interpret non-verbal signs, such as facial expression, posture, or gestures to comprehend a patient’s emotional state and choose the right words to alleviate stress and anxiety.
Unlike traditional counseling where you need to schedule and travel for appointments, AI-based and other mental health apps allow users to access therapeutic help anywhere, anytime. Moreover, they provide help at little or no cost, compared to in-person therapy rates, missed work, and the need to make other arrangements and commute.
AI-based apps remove accessibility barriers to mental health treatment as staff shortages across the board and a lack of providers in rural and remote areas. Also, artificial intelligence algorithms for mental health care have already been proven to be successful in detecting symptoms of depression, PTSD, and other conditions by analyzing behavioral signals.
Eradicating Mental Health Issues
Making mental health diagnoses and treatments more quantifiable and less subjective could ultimately help eradicate these conditions and improve outcomes. There’s no blood test for mental health conditions, but a machine learning algorithm could become a kind of equivalent a research-based, “objective” test that makes the need to seek treatment less about a patient’s subjective experience of distress and more about evidence-based data and medical best practices.
Being a Support System to the Therapist
AI could be an effective way for clinicians to make the best of the time they have with patients. This is because AI can track and analyze substantial amounts of data faster and even more efficiently than any human. As a result, algorithms help with more accurate diagnoses. They can also spot early signs of trouble by monitoring the patient’s mood and behavior and alert clinicians so that they can quickly adjust treatment plans.