Over the past few years, Artificial Intelligence has been either aiding or replacing humans in almost every industry. From using chatbots for 24/7 customer support to using robots for delivering groceries to the doorstep of customers, AI has become an integral part of every industry vertical. AI integration is becoming inevitable across all industries and the healthcare sector is not an exception to this, especially mental healthcare.
Mental health is on the verge of being a global crisis, as the prevalence of mental disorders is rapidly increasing. In 2020, the number of people living with anxiety and depressive disorders rose significantly, due to the COVID-19 pandemic.
Since the COVID-19 pandemic, people are increasingly seeking help for mental health problems, such as depression and anxiety, as the pandemic triggered a 25% increase in the prevalence of anxiety and depression worldwide. This unexpected surge in the prevalence of mental health problems is pressurizing the already-stretched healthcare and therapeutic services, which have become increasingly difficult for people to access. Could AI-driven and machine learning-enabled technology change the course of mental healthcare once and for all?
Is Artificial Intelligence the Future of Mental Healthcare?
Researchers, clinicians, and therapists are increasingly finding AI as a powerful tool in mental healthcare. This field is set to alter how mental illnesses are perceived and treated fundamentally. As technology advances, AI-driven tools are becoming more accessible to industry professionals to help them develop new platforms for mental well-being. Industry professionals are already changing lives and helping patients suffering from various mental health conditions by using revolutionary technologies and AI-enabled platforms such as:
Chatbots are already being used in almost every industry for activities ranging from increasing customer engagement to balancing automation with a human touch. They are employed by companies for offering mental health support and treatment. These bots are designed to help mental health patients.
Chatbots used as AI therapists are based on a methodology called Cognitive Behavioral Therapy (CBT), a known therapy used to treat anxiety and depression. This process involves behavioral and thinking patterns based on responses that patients give during therapy.
Chatbots can address mental disorders such as anxiety and depression, as they are programmed to detect specific keywords during the conversation. With Natural Language Processing (NPL) algorithms and patient response, these bots follow a strategic approach to encourage patients to redirect their thoughts.
Platforms such as Woebot, a popular therapy chatbot, help deliver timely mental support to patients. It uses a text message interface where patients can respond using emojis, thereby helping patients to redirect their thoughts through journaling, reframing negative thoughts, and providing CBT activities.
Wysa is an anxiety therapy chatbot that listens to patients and guides them in managing their anxiety and stress-related symptoms. Wysa's AI conversational care guides users through curated CBT programs and on-demand support.
Therapy chatbots are the next big thing in psychotherapy. It is generating various possibilities in the mental health industry. Companies are integrating conversational AI for therapy purposes. Therapy chatbots are helping mitigate the mental health crisis.
Early Diagnosis and Predicting Patient Outcomes
AI has already been used for analyzing the medical and behavioral data of patients for early diagnosis and predicting patient outcomes. With this data and the rising use of advanced machine learning, red flags of potential mental problems are raised before the disorder progresses to an acute stage.
In 2017 IBM and the University of Alberta publish new data on machine learning algorithms to help predict schizophrenia. The study finds that AI and machine learning algorithms helped predict instances of schizophrenia with 74% accuracy. This research was based on 28 studies that were reviewed, including electronic health records, data taken from smartphones and video monitoring systems, brain imaging data, and social media data.
Another study is underway in The Alan Turning Project named, “AI for precision mental health: Data-driven Healthcare solutions.” This project started in 2021 and is focused on developing a cost-effective clinical decision support system to help clinicians.
Researchers are analyzing large-scale datasets from individuals who have not shown any signs of mental health symptoms to ones that are suffering from mental disorders such as PTSD, schizophrenia, and dementia, and guiding clinicians in patient selection for clinical trials to enhance their efficacy and pave the way to drug discovery.
CBT is commonly used to treat anxiety and depression. It is also useful for treating other mental and physical health problems. In a study conducted in JAMA Psychiatry in 2019, AI has been tested for mental health symptoms and to predict cases where patients are likely to respond to CBT. This study can be used to validate the use of effective of CBT as a treatment and potentially reduce the need for medications.
Better Patient Compliance with Personalized Treatment
In mental healthcare, personalized treatment is highly preferred by healthcare professionals. AI has been used to monitor symptoms and responses to treatment and provide insights that are used to adjust personalized treatment plans.
In 2020, a study was conducted by the University of California, Davis, where AI was used to treat teenagers with schizophrenia. This clinical trial used brain images of enrolled teenage schizophrenia patients with specific patterns. Based on these patterns, AI was used to recommend treatments.
Challenges and Opportunities
The presence of all these advanced technologies in the market indicates that AI has a promising future in the mental healthcare industry, but some specific challenges need to be overcome by AI researchers and healthcare professionals.
One of the biggest challenges for AI integration is AI bias treatment. AI is used to find behavioral patterns and make early predictions, as these models are trained with large amounts of datasets. Inaccuracies and imbalances in these trained datasets could lead to complexities in the early prediction of disorders and treatment plans.
Mental health issues need more subjective judgments from industry experts than AI-enabled platforms. Patients using these platforms need to make decisions based on self-reported feelings instead of industry experts. This potentially leads to uncertainty around the diagnosis and raises the need for careful monitoring to ensure the best patient outcomes.
A recent World Health Organization report on the challenges associated with the use of AI in mental health treatment and research found that there are still “significant gaps” in understanding how AI is implemented in mental healthcare. This is coupled with inadequate assessment of associated risks of bias and flaws in how data is processed in existing AI healthcare applications.
Overall, AI has the potential to create a positive impact in many areas of mental healthcare. Some significant gaps need to be overcome, and this progress can be made with care, proper methodology, and technologies present in the industry.