Appliances of AI use in Mental Health

Mental healthcare professionals can actively employ various AI-driven client engagement strategies to effectively guide the recovery journey for individuals grappling with mental health issues. For instance, AI technologies can be seamlessly integrated into mobile applications to send timely reminders for medication schedules, track side effects, monitor medication responses, enhance adherence, and facilitate collaboration between individuals and their healthcare providers. AI-driven apps can also monitor mood fluctuations and offer insights into potential triggers, enabling individuals to identify patterns and make informed decisions about self-care.

• Intervention: How AI can assist in the intervention and treatment of mental health disorders?

Modern AI and machine learning, in particular, present extensive possibilities for advancing prediction, detection, and treatment solutions in the domain of mental health care. This technology has the capacity to not only assist mental health practitioners in redefining mental illnesses more objectively than the current DSM-5 framework but also to identify these conditions at earlier, prodromal stages when interventions can yield maximum efficacy. Moreover, AI enables the personalisation of treatments based on an individual's unique characteristics. In the context of prediction and prevention, AI can evaluate the risk of developing specific mental health disorders based on an individual's profile, genetic predisposition, and environmental factors. This enables proactive measures to prevent or mitigate the onset of disorders. An AI-based decision support system (DSS) has been developed, efficiently detecting and diagnosing various mental disorders. AI algorithms can sift through diverse data sources, such as electronic health records, diagnostic tests, and behavioural patterns, for early detection of mental health disorder signs, allowing timely intervention, and improved prognosis.

Positive impact on cognitive aspects

The inaccuracy and subjectivity of cognitive assessments have led many healthcare professionals to explore tools and techniques that would automate these processes, making them more objective and increasing the efficiency of facilitation. AI has emerged as one of the most promising approaches to automate cognitive assessments. Cognitive assessments involve a series of tasks and tests that are typically designed to evaluate areas of cognitive function like language, reasoning, memory, decision making, attention and perception. These assessments are usually administered by healthcare professions like occupational therapists, neurologists or psychologists.

Screening of cognitive deficits or impairments and early intervention is currently the most widely accepted strategy to manage a number of psychological disorders. The diagnosis of these is established through thorough assessments, which may also help in understanding cognitive pathophysiology.  However, lack of proper standardized screening and guidelines often leads to undiagnosed cognitive impairment which further leads to increased disease progression and cognitive decline.

Automating the assessment and prediction process is the key to timely diagnosis and management. The advent of AI has resulted in automated assessment techniques which improve the accuracy of diagnosis. ML and AI-based approaches like Support Vector Machine (SVM), neural networks and ensemble techniques like Convolutional Neural Network (CNN), Alex Net, Google Net and LeNet5 have yielded some of the best results and accuracies when it comes to the use of AI for the assessment of cognitive mental health disorders.

The evolution of AI has contributed effectively to the early detection, diagnosis, and referral management of mental health disorders because experts are limited in regard to their performance, knowledge diversity and daily exertion which can also affect their performance.

Intellectual and developmental disorders

These include disorders like cerebral palsy, down syndrome, ADHD, autism spectrum disorders and fragile X syndrome which typically appear in a child before the age of 18. Data from different sources form inputs for the analysis of intellectual and developmental disabilities. The neuroimaging data are analyzed with a DNN to detect the presence of ID or DD in children. Based on this, AI-assisted screening systems have been developed to analysed the electronic health record of individuals for the detection of various disabilities and disorders. Machine learning can be used to detect the presence of disorders like ASD using eye movements. In addition, AI finds its role in the detection of ASDs from the presence of maternal and blood autoantibody-based biomarkers.

Neurodegenerative disorders

Major neurodegenerative disorders like Alzheimer's, Parkinson’s and motor neuron disease possess a great challenge in that their symptoms are not seen until a substantial number of neurons are lost. Against this, early diagnosis is difficult. Using machine learning algorithms to analyse MRI images, early detection of these diseases is made easier. A study by Kloppel et al. showed that Support Vector Machine (SVM) can use MRI scans to efficiently differentiate between individuals with Alzheimer's and those with frontotemporal lobar degeneration. Furthermore, they also helped distinguish between healthy individuals and those with Alzheimer's. 3D neural network architectures have also been used for the detection of Alzheimer's in the past.

Seizures

Epileptic seizures typically develop with a sudden abnormal surge of electrical activities in the brain. The detection of these seizures can prove to be a real challenge due to the variability in their pattern. Previous studies have demonstrated how electroencephalography recordings have been analysed using machine learning algorithms for the effective detection of seizures.

Positive Impact on affective & emotional aspects

The integration of AI and affective computing has given rise to a realm known as Emotional AI, wherein technologies are designed to perceive, learn from, and interact with human emotions. Despite being in its nascent stages, Emotional AI is gaining prominence in various facets of daily life, from personal devices to professional domains. It is even influencing the emotional ambiance of spaces like workplaces, hospitals, and classrooms. Emotion sensing, a pivotal aspect of Emotional AI, traces its origins to affective computing in the 1990s. Enabled by weak, narrow, and task-based AI, Emotional AI aims to comprehend and interact with emotional states by analysing a spectrum of data related to words, images, facial expressions, gaze direction, gestures, voices, and physiological signals, such as heart rate, body temperature, respiration, and skin conductivity. The input features for emotion recognition could include facial expressions, voice samples, or biofeedback data, while the output encompasses emotional states used for various purposes. Common machine learning techniques like convolutional neural networks, region proposal networks, and recurrent neural networks are frequently employed for these tasks. These emotional states are then utilized to enhance interactions with devices and media content, intensify artistic expression, facilitate surveillance and learning, and enhance self-understanding of moods and well-being.

This technology transcends human limitations, enabling more nuanced and accurate detection of emotional signals through wearables and smartphone applications. Furthermore, AI facilitates emotion regulation through tailored interventions. On the basis of real-time emotion data, AI-driven cognitive computing systems can offer immediate strategies, interactive exercises, and simulations for managing emotional states. For instance, AI-powered applications can guide users through relaxation exercises, deep breathing techniques, or mindfulness practices for reducing perceived stress and improving self-regulation.

The role of AI in emotional well-being extends beyond mere detection and regulation. AI-powered systems can also contribute to the development of emotional intelligence, a key factor in maintaining balanced emotional states. By providing users with insights into their emotional patterns over time, AI empowers individuals to cultivate a deeper understanding of their emotions and triggers, equipping individuals with valuable skills for effectively managing emotions and fostering healthier responses to challenges. Furthermore, AI-driven interfaces and applications can adapt their responses based on users' emotional cues, providing empathetic and supportive interactions. These emotionally intelligent interfaces offer a human-like connection, enhancing users' emotional experiences and addressing their emotional needs.

Emotional dysregulation

Difficulties in effectively managing and expressing emotions, often lead to heightened emotional responses, mood instability, and impaired emotional functioning, increasing the risk of depression, anxiety, and substance use disorders. Music-based emotion regulation mobile app has been developed with the aim of teaching emotion regulation skills to individuals with mental health problems in clinical and community settings, including eating disorders, anxiety disorders, substance misuse, and schizophrenia. AI-powered therapeutic games and virtual reality experiences can also provide immersive environments for practicing emotion regulation skills. These interactive platforms offer safe spaces to explore and manage emotions, allowing individuals to gradually build their emotional regulation capacities. Furthermore, AI-enabled biofeedback and neuro-feedback systems can help individuals gain awareness and control over their physiological responses associated with emotional dysregulation. These systems provide real-time feedback on heart rate, brain activity, and other physiological indicators, allowing individuals to learn how to modulate their emotional responses. Additionally, immediate and empathetic support can be offered by AI-driven virtual therapists or chatbots for individuals experiencing emotional dysregulation.

Mood disorders

The use of AI techniques holds significant potential in improving the diagnosis of mood disorders as well as identifying suicide risks. A combination of using mobile and wearable technology can assist in collecting physiological and behavioural markers followed by the AI to analyse these data that can provide objective markers for conditions such as depression and bipolar disorder (BD). AI also has the capacity to monitor social media and text messages for linguistic cues and sentiment analysis, enabling the prediction of mood fluctuations and potential relapses. For example, the Social Rhythm Metric (SRM), a clinically validated marker of stability and rhythmicity for individuals with bipolar disorder (BD), can be automatically assessed using passively sensed data from smartphones. This personalized monitoring enhances self-management and reduces the risk of severe episodes.

Autism spectrum disorder (ASD)

AI-driven tools assist in early detection by analysing facial expressions, eye gaze, and gestures during video-based interactions. Smart tablet technology can provide a new paradigm for clinical autism assessments, aiding in the identification and intervention of ASD in young children. The capability of this technology extends to its integration with customized gaming experiences that incorporate various psychometric assessments. Additionally, it has the capacity to utilize sensors in innovative manners, including evaluating social intelligence or detecting emotional responses through the front-facing camera or by combining gameplay with sensor-equipped toys for enhanced functionality.

Schizophrenia

Automated speech analysis can assess speech patterns and prosody to detect subtle and clinically relevant affect-related changes in speech that might indicate the onset of schizophrenia symptoms. NLP-driven tools can also assist in tracking and predicting relapses based on speech patterns and emotional content. AI interventions can play a crucial role in promoting emotional well-being among children and adolescents. Interactive and engaging AI-driven platforms can provide age-appropriate tools for emotional regulation and stress management. These platforms may include virtual reality experiences, interactive games, and personalized virtual companions. AI can also monitor online activities and social media usage to detect signs of cyberbullying, anxiety, or depression in young users. Early intervention through AI can help develop healthy emotional coping mechanisms and prevent the escalation of mental health issues.

For young adults transitioning into adulthood, AI applications can offer support in managing the stresses of higher education, career choices, and independent living. AI-powered virtual mentors can provide guidance on stress reduction techniques, time management, and decision-making. Additionally, AI-driven platforms can curate resources for building resilience, coping with life changes, and maintaining a healthy work-life balance. Middle-aged adults often face increased responsibilities and societal pressures. AI can assist this group by offering personalized stress management strategies, facilitating relaxation techniques, and providing reminders for self-care activities. AI-powered chatbots or virtual therapists can offer a confidential space for discussing emotional concerns, and AI-based wellness platforms can tailor fitness and wellness routines to individual preferences and schedules.

The emotional challenges in the elderly can be effectively addressed by AI applications, including feelings of isolation, cognitive decline, and age-related mental health issues. AI-driven virtual companions can offer companionship and engage older adults in cognitive exercises and reminiscence therapy. Additionally, AI-powered sensors can detect changes in behaviour patterns and alert caregivers or healthcare providers to potential emotional distress or cognitive decline.

While acknowledging the substantial strides made by AI in enhancing mental health care, it is important to scrutinize these advancements and address the nuanced concerns surrounding their implementation. Bridging the gap between the perceived benefits and potential drawbacks of AI applications in mental health outcomes requires a critical evaluation.

Transparency: AI algorithms used in mental healthcare should be transparent and explainable. Patients and healthcare providers need to understand how decisions are made by AI systems to ensure accountability and to build trust.

Bias and Fairness: AI algorithms can inherit biases present in the data they are trained on, leading to potential disparities in diagnosis and treatment recommendations. Efforts must be made to identify and mitigate these biases to ensure fair and equitable care for all individuals.

Cognitive Neuroscience and AI: Unlocking the Future of Intelligence

Cognitive neuroscience is the scientific study of how brain structures and processes underlie mental functions such as perception, memory, decision-making, and consciousness. With recent advancements in artificial intelligence (AI) and machine learning (ML), this field has gained renewed interest. AI, particularly neural networks, is increasingly inspired by biological processes, while cognitive neuroscience is drawing upon machine learning techniques to model and predict neural activity. This article explores the interrelationship between cognitive neuroscience, AI, and machine learning, examining how insights from the brain are shaping AI development and how AI is, in turn, revolutionising the way we understand the brain.

The Intersection of Cognitive Neuroscience 

Cognitive neuroscience focuses on how brain activities translate into complex behaviours such as reasoning, learning, and emotion. With the rise of artificial intelligence and machine learning, these fields have begun to intersect in fascinating ways. AI, especially through techniques like artificial neural networks, seeks to mimic human-like intelligence, drawing inspiration from the brain’s structure. Meanwhile, machine learning techniques are enabling cognitive neuroscientists to handle vast datasets, allowing them to make predictions and discover patterns in brain activity that were previously unreachable. This synergy between cognitive neuroscience, AI, and machine learning is rapidly advancing our understanding of both the brain and intelligent systems.

Suicide prevention 

Finding effective strategies for suicide prediction and prevention is of paramount importance. Traditional methods of assessing suicide risk rely heavily on clinician judgment, which, while vital, can sometimes fall short. There is room for improvement and innovation. Artificial intelligence has recently demonstrated promising advances in suicide prevention.  AI systems can analyse patient data to identify patterns and other nuanced suicide risk factors. This emerging research shows the potential for more precise and early intervention strategies with encouraging clinical implications.

Methods: The study used machine learning (ML) and deep learning (DL) to detect suicidal thoughts in social media posts from platforms like Twitter and Reddit. First, posts were collected and labelled by researchers as suicidal or non-suicidal based on keywords and patterns. The text was next cleaned and converted into numerical data for analysis. ML models and advanced DL were trained to identify patterns linked to suicidal ideation.