This article will examine the types of artificial intelligence and its various applications in the field of mental health and psychotherapy. Since the inception of Artificial Intelligence (AI) as a discipline in 1950, people have been asking the same question: What is AI?
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Many have attempted to define and describe AI, but most definitions can be narrowed down to those that view AI as the creation of computer programs or the concept of machines capable of human-like behaviour. John McCarthy, the founder of this discipline, described the process as “making a machine behave in ways that would be called intelligent if a human were so behaving.” In his opinion, the idea behind AI was to develop machines that behave as if they were intelligent.
In general, since humans have many definitions of what intelligence is, it is difficult to narrow artificial intelligence to just one definition. AI was designed as a computer system that resembles the human mind in many ways and is capable of performing tasks characteristic of humans, such as reasoning, problem-solving, learning, and drawing conclusions. Therefore, it is fair to say that AI is an umbrella term encompassing a broad range of approaches and techniques for developing computer systems that perform cognitive processes and tasks characteristic of humans.
Now, after almost 60 years of research, we can say that we have entered the age of AI, and it has found a place not only in many industries and disciplines but also in human consciousness.
Since the COVID-19 pandemic, more people are seeking help for mental health issues such as depression and anxiety, putting pressure on already strained healthcare services. Suicide is now the fourth leading cause of death among 15 to 29-year-olds globally. Could AI-powered technology help reduce the need for medication or hospitalisation in mental health care, offering new ways to improve patient outcomes?
An increasing number of clinicians, therapists, psychiatrists, and researchers are recognising the value of artificial intelligence (AI) as a powerful tool for delivering behavioural health care. With growing evidence suggesting that AI can help diagnose diseases, develop therapies, and enable more personalised approaches and treatments, the implementation of AI in mental health is rapidly expanding.
In this article, we will introduce three aspects of uses of artificial intelligence in mental health to showcase how it can be successfully deployed to assist professionals in their daily work in helping others:
One of the biggest challenges with mental health issues is the lack of awareness and, therefore, acceptance of this important topic that affects so many. Here, AI can be helpful, as it can increase awareness of mental health issues by leveraging technologies such as natural language processing, sentiment analysis, and data mining. These tools help spread accurate information, reduce stigma, and promote conversations around mental health. Additionally, AI can analyse social media data to monitor public sentiment and identify key topics for targeted awareness campaigns. AI-driven chatbots can provide personalised support by offering guidance, coping strategies, and access to professional resources. Furthermore, AI-powered platforms enhance psychoeducation by tailoring content to users' needs, improving engagement and understanding of mental health topics.
When we think of AI use cases in mental health, we often assume that it will replace psychiatrists, psychologists, or other mental health professionals. However, the role of AI is not to replace anyone but to assist and support. For example, it can support individuals with mental health concerns by complementing traditional care methods. It can be integrated into mobile apps to send medication reminders, track side effects, and monitor mood fluctuations, helping individuals manage their mental health.
AI-driven tools are also capable of analysing digital information, such as social media content and medical records, to detect behavioural shifts that may indicate mental health issues. For instance, a drop in physical activity tracked by a smartwatch could signal potential depression. In online communities, AI can foster connections among individuals facing similar challenges by facilitating online support groups, enabling individuals to connect and share coping strategies. Finally, as with other medical and health-related issues, AI applications can track progress and provide feedback, fostering motivation throughout the recovery process.
AI can significantly assist in the intervention and treatment of mental health disorders by improving prediction, detection, and personalised care, which are crucial parts of any therapy. By evaluating an individual's unique characteristics, risk factors, and environmental influences, AI can personalise treatments and identify mental health conditions at early stages, enabling timely interventions for better outcomes. Another application of AI involves chatbots that use natural language processing to assess mood, stress, and sleep patterns, offering therapeutic recommendations or prompting individuals to seek medical help. Furthermore, AI-powered tools offer accessible, on-demand mental health support, especially for those with limited access to conventional therapy or those who wish to enhance traditional therapy with cognitive exercises.
Mental health professionals have long sought tools and systems to enhance the accuracy and objectivity of cognitive assessments. AI has made significant strides in improving these assessments by automating the processes—i.e., a series of tasks and tests typically designed to evaluate areas of cognitive function such as language, reasoning, memory, decision-making, attention, and perception.
AI can also help with the early detection and diagnosis of cognitive impairments, which is crucial for managing psychological and neurodegenerative disorders. AI-driven tools, such as neural networks and machine learning algorithms, can analyse data from cognitive tests, neuroimaging, and electronic health records, leading to more accurate and timely diagnoses. These technologies are particularly effective in identifying intellectual disabilities, developmental disorders, and neurodegenerative conditions such as Alzheimer's and Parkinson's. Machine learning algorithms can also be used to detect epileptic seizures by analysing brain activity patterns.
Nowadays, psychiatrists or psychologists can use Emotional AI, which integrates AI and affective computing to perceive and interact with human emotions, thereby enhancing emotional well-being across different areas of life. This technology uses emotion-sensing techniques, such as analysing facial expressions, voice tones, and physiological signals like heart rate, to gain deeper emotional insights. It is already successfully applied to areas such as emotional dysregulation, mood disorders, Autism Spectrum Disorder (ASD), and Schizophrenia. These applications are adaptable to different age groups, offering stress management resources for young adults, emotional support for middle-aged individuals, and companionship for the elderly.
Through virtual therapists, chatbots, and emotional support systems, AI offers accessible mental health support to those with limited access to traditional therapy. Emotional AI is revolutionising emotional care by providing real-time support, fostering emotional understanding, and addressing a wide range of emotional challenges.
While AI shows promise in predicting mental health issues and personalising treatments, several challenges must be addressed. AI bias is a major concern, as algorithms trained on imbalanced datasets could lead to inaccurate predictions or reinforce social prejudices, particularly for underrepresented groups. Collaboration between AI engineers and mental health professionals is crucial to mitigate these biases. Diagnosing mental health conditions relies heavily on subjective self-reports, making AI diagnosis less straightforward and requiring careful follow-up. Furthermore, a World Health Organization report highlights gaps in understanding AI’s role in mental healthcare and calls for better evaluation of risks, including bias.
Another major issue is data security and privacy protection, as AI needs to be fed information in order to learn. This is a critical topic in mental health, as the data shared with AI are intimate and personal. However, to provide the personalised treatment mentioned above, AI requires access to personal information. Therefore, strict measures should always be implemented to ensure the safety of patient data:
At Digital Samba, we live and breathe technology, and we have created our software to contribute to the growing demand for cultivating and maintaining mental health while making access to it easier. With the Digital Samba video meeting solution, we enable patients to virtually connect with psychologists, offering them a “human” option for therapy.
To keep up with AI trends and make the work of psychologists easier and more automated, we have recently introduced transcriptions and session summaries that can be easily accessed in the Digital Samba dashboard once an account is created. As discussed above, we take privacy and data security very seriously, especially regarding the integration of artificial intelligence tools. We use AI responsibly, as the providers we work with are European companies that comply with GDPR.
These features are just the beginning of our journey towards exploring how we can smartly yet responsibly employ AI for our customers in the mental health sector. We work closely with them, listening to their needs and adapting our services accordingly. We are cautiously following the development of AI and its use in psychology and psychiatry, with a desire to contribute even more to the field of online psychotherapy and make this vital area of health and self-care available to all who need or want to embrace it.
Create your free account today and test our artificial intelligence features that will allow you to dedicate more time to your patients and less to administrative tasks.
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