COVID-19 has exposed the population to a wide range of mental diseases, particularly in light of social distance and insufficient mental health support. The rise of AI signals not simply a shift in mental health diagnosis and treatment, but also in how we identify mental health conditions. Artificial intelligence (AI) has a lot of potential in the realm of mental health assessment and therapy.
Chatbots for cognitive behavioral therapy, intelligent virtual worlds and artificial companions, augmented reality applications, therapeutic computer games, and electronic medical records are some of the ways AI improves mental health. These opportunities, however, come with a slew of issues, including user privacy, data security, bias, consent, governance, and regulation. With so many AI solutions available, psychologists and psychiatrists must choose the best technology for their goals, resources available, and ease of application. There is a need to develop, test, and validate indigenous unique mental health technology. Over time, a balance between conventional and technologically based treatment will be achieved.
Table of Contents:
What is artificial intelligence and how does it work?
One of the most frequent types of artificial intelligence in mental health is machine learning. There are various variations of this broad technique, which is at the heart of several approaches to AI and healthcare technology.
Precision medicine is the most widely used use of traditional machine learning in the field of artificial intelligence in healthcare. For many healthcare organizations, being able to forecast which treatment techniques are likely to be successful with patients based on their makeup and treatment framework is a big step forward. The bulk of AI in mental health app that use machine learning and precision medicine require data for training with a known end result.
Deep learning-based artificial intelligence in healthcare is also employed for speech recognition in the form of natural language processing (NLP). Because features in deep learning models often have little value to human observers, deciphering the model’s results without adequate interpretation can be difficult.
What is mental health?
Mental health is defined by the World Health Organization (WHO) as a “state of mental well-being in which an individual recognizes his or her own potential, can cope with typical life stresses, can work successfully and fruitfully, and can contribute to his or her community.”
What function does artificial intelligence have in mental health?
Artificial Intelligence (AI) is becoming more frequently employed in medicine for physical health applications, but it has been slower to adopt AI technology in mental health. Mental health practitioners, in comparison to most non-psychiatric practitioners, are more hands-on and patient-centered in their clinical practice, relying on “softer” talents like forming connections with patients and closely monitoring patient behavior and emotions.
In the field of mental health, clinical data is frequently collected in the form of subjective and qualitative patient comments, as well as written notes.
In the subject of mental health, AI technology offers a lot of promise. AI has the potential to revolutionize how we detect and comprehend mental illnesses. An individual’s bio-psycho-social profile is best suited to completing a person’s holistic mental wellness. Our understanding of the interactions between these biological, psychological, and social systems, on the other hand, is limited.
The etiology of mental illness is extremely diverse, and the discovery of biomarkers could lead to more objective and accurate classifications. Researchers may be able to develop better pre-diagnosis screening tools and risk models using AI techniques to assess a person’s inclination for, or risk of developing, a mental condition. As a long-term goal, we need to apply computational tools best suited to massive data to give individualized mental healthcare.
AI enables early or prodromal detection of mental diseases, when interventions may be more effective, and personalization of treatments based on an individual’s unique traits. Nonetheless, caution is required to avoid overinterpreting preliminary findings, and more effort is required to bridge the gap between AI research and mental health clinical care.
Is artificial intelligence the future of mental health?
Existing medicines can be provided in novel ways thanks to AI, potentially boosting their availability and efficacy.
Uses of artificial intelligence in mental health
Internet-based cognitive behavioral treatment chatbot: Although Internet-based Cognitive-Behavioral Therapy (CBT) has been available since the 1990s, little adherence has been reported. The development of CBT chatbots, which use a conversational manner to deliver CBT, may increase adherence and provide other benefits. One study among Japanese employees found an increase in anxiety and alcohol use, which the authors attributed to a greater mental health awareness of deviant thinking and drinking behavior.
A chatbot is computer software that uses a text or voice-based chat interface to simulate human interaction. The underlying system could be built on anything from a set of simple rule-based replies and keyword matching to advanced Natural Language Processing (NLP) and Machine Learning (ML) techniques.
NLP refers to the use of computers to read and manipulate natural language, whereas ML refers to self-learning computer systems that can develop and adapt to new data without being explicitly programmed to do so. Regardless of the genuine intelligence of the replying bot, the experience of a user submitting data and a bot responding is distinct.
Because a bot recognises common speech patterns, it can give the user the impression of being in a real environment. An app or a web search responds to a user’s search query directly, but a bot simulates a real-life conversation as if the user were speaking with another person; the feature’s uniqueness rests in the user’s perception of the interaction.
Aside from the difficulty of empowering chatbots with AI in terms of their ability to mimic the structures of natural language conversation, another critical feature, particularly in a psychology/therapy approach, is emotional intelligence, or the ability for chatbots to detect and respond appropriately to a person’s emotional state. Some recent work on emotionally intelligent AI has come from effective computing.
There are a variety of other intriguing usage choices, ranging from a simple conversational search to a chatbot that is the equal of a real mental health specialist. While bots that can hold a rudimentary conversation beyond one question, one input, and one response are not yet smart enough to imitate a therapist, they are viable and have been used for a variety of purposes, including as a virtual dietitian for diabetic patients, students as an educational system, and as an e-learning system for disabled people to learn how to speak.
The employment of a chatbot like this would enable the collecting of more conversational content from the user. The capacity to examine a bigger volume of dialogue should likely lead to better content suggestions.
Artificial companions and intelligent virtual worlds: Virtual reality simulation is another AI application that is gaining pace. Virtual reality is a sort of human-computer interaction in which the user can immerse oneself in and interact with a computer-generated virtual environment. Clinical virtual reality is the use of virtual reality for clinical assessment and therapy; it has also been used to treat a variety of psychological diseases.
AI is already being used in virtual worlds to create intelligent things that can learn and interact with people, improving their adaptability and realism. Furthermore, these artificially intelligent organisms are now capable of displaying emotion and conversing with humans.
Virtual companions such as virtual home pets, may improve mental health and help people cope with loneliness. On a video screen, these can take the form of virtual animals or humanoid robots. Dementia patients, for example, have had animal robot friends made to help them. Much like AI-augmented video games, AI makes these artificial companions more lifelike, entertaining, and capable of doing things that are responsive to a patient’s needs.
Augmented reality applications integrate virtual reality and the real world together by superimposing computer-generated visuals over live camera imagery.
This technology, when combined with other AI technologies, has the potential to change how people perceive and interact with their surroundings, as well as be used for a variety of therapeutic purposes. It may, for example, be used to generate anxiety-inducing virtual stimuli in the patient’s real-world environment during prolonged exposure therapy or to offer patients with real-time therapeutic virtual coaching on the screen.
Augmented reality and other AI capabilities can be used on mobile devices such as smartphones, tablet PCs, and other wearable devices. Google Glass, a wearable intelligent glass, for example, might connect people to the Internet for real-time data access and sharing, among other things.
Researchers at the University of Washington and Aalto University in Finland are working on bionic contact lenses, which could one day lead to technology that allows individuals to scan the Internet and retrieve data on demand, such as medical information.
Therapeutic computer games: In mental health therapy, computer games can be utilized for skill teaching, behavior modeling, therapeutic diversion, and other therapeutic goals. Computer games can improve patient engagement, treatment adherence, and lessen stigma associated with mental treatment, to name a few therapeutic benefits. The usage of therapeutic computer games has also been shown to improve adolescent self-confidence and problem-solving ability.
AI technology is employed in many commercial computer games, and it has recently been applied in Internet-based online and social network games. AI and machine learning technology improves realism in computer games, making them more engaging, demanding, and fun to play. Machine learning techniques can also help to tailor the games to the needs of the patients.
Virtual intelligent agents can train patients in games or other virtual settings like Second Life, or AI technology can be used to direct the gaming so that the patient can practise skills in areas where they are needed. Brigadoon, for example, is a virtual world in Second Life built exclusively for those on the autistic spectrum. In the simulation, users can interact with avatars to learn and practise social skills in a secure environment.
Artificial Intelligence in AI integration into many clinical equipment used by mental health and other medical practitioners can improve convenience, accuracy, and efficiency. For a long time, speech recognition technology has been used for medical dictation.
A computer database that allows healthcare management and physicians to record information about patients is known as an Electronic Medical Record (EMR). When opposed to paper-based individual documentation, EMRs are increasingly being employed by government and private medical providers because of their efficiency and accuracy in recording.
According to the National Center for Health Statistics, roughly 85.9% of American doctors use electronic health/medical records in their offices. However, EMR software solutions that use Artificial Intelligence (AI) and Boolean logic to automate patient data input by recalling components from past cases that are the same or similar to the present case are now available, boosting accuracy and saving time.
Concerns about client privacy arise when adopting an EMR system, such as how much information should be maintained in an EMR and how accessible that record is to experts within an organization.
For example, because other health professionals and administrators have access to client information, any document prepared by mental health specialists can be released to all connected and unrelated physicians and administrators unintentionally or purposefully. As a result, experts emphasize the importance of following fair information practices in order to avoid any breaches of patient digital privacy.
Another use may be an AI-based computer that listens to therapy or assessment sessions and intelligently summarizes them, eliminating the need for clinical record notes at the end. Smartphones, tablets, and other AI based mental health platforms could all benefit from this technology.
Conclusion
How is AI used in mental health? In the field of mental health care, artificial intelligence technology has both immense potential and considerable challenges. Artificial intelligence’s successful incorporation into healthcare could have a big impact on care quality.
In psychology, new technology for diagnosis, monitoring, and therapy may improve patient results while also redistributing practitioner workload. While there is a lot of potential, there will also be a lot of risks and stumbling blocks. Careful navigation will be required to ensure proper uptake of this new technology.
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