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E-Consult

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E-Consult is a completely free online matching consultation service that allows physicians who are struggling with patient diagnoses or treatment plans to consult with specialists in relevant fields who may not be nearby. By addressing the primary physician's questions through this product, the aim is to achieve early diagnosis and treatment optimization in rare and complex disease areas.

ASI (Artificial Superintelligence) refers to the concept of an intelligence that surpasses human cognitive abilities across all domains. It represents the final stage in the evolution of artificial intelligence, beyond current AI and even AGI (Artificial General Intelligence). ASI is envisioned as having capabilities that far exceed those of humans, allowing it to perform any intellectual task more efficiently and accurately.

Here are key characteristics often associated with ASI:

  1. Superior Intelligence in All Domains
    ASI would outperform humans in all areas, including science, art, ethics, and problem-solving.

  2. Self-Improvement Capabilities
    ASI could potentially improve its own algorithms and architecture, leading to exponential growth in its intelligence.

  3. Ethical and Philosophical Challenges
    The development of ASI raises numerous ethical concerns, such as how to ensure its safety and maintain human control over it. There is concern that ASI, if left unchecked, could pose existential risks to humanity.

The idea of ASI is often depicted in science fiction, but realizing it remains a long-term and highly debated goal. Although ASI has not yet been achieved, the rapid progress in AI research suggests that it could eventually become a reality, prompting ongoing discussions about its implications.

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The technology for quantifying a person's mental state involves using AI for emotion recognition based on physiological data such as heart rate, electrodermal activity, blood pressure, breathing patterns, and brain waves. This technology aims to understand emotions through advanced algorithms, natural language processing, and machine learning, enabling AI to sensitively and accurately recognize human emotions and respond accordingly.

Emotion recognition AI is crucial in various fields like mental healthcare, marketing, and customer service. For example, AI systems provide mental health support through personalized interactions, leveraging machine learning to understand user emotions. This technology enhances the effectiveness of therapy by offering scalable and accessible solutions. In customer service, emotion recognition AI improves user interactions by responding to the emotional state of the customer, thereby increasing satisfaction and loyalty.

In mental health, AI's evolution from data-driven diagnostic tools to emotionally intelligent systems has opened new possibilities. Technologies which detects non-verbal cues and voice tones, are designed to support patients with conditions like PTSD, anxiety, and depression. These systems help identify emotional states that may be overlooked by traditional methods.

Empathy-driven AI not only improves interactions in healthcare but is also being applied in other areas such as education, where it can adapt learning experiences based on the emotional needs of students. In social work, it helps prioritize individuals who need urgent care by assessing emotional states.

Despite its benefits, emotion recognition AI faces challenges, such as privacy concerns, since it involves the use of sensitive emotional data. Ensuring the security of this data and creating legal frameworks to manage its use are crucial for societal acceptance.

Global Mental Health Services and the Impact of Artificial Intelligence–Powered Large Language Models
There is a large and growing need for mental health services worldwide, but there is a massive shortage of mental health specialists to meet these needs—particularly in humanitarian emergencies, low-income countries, and other areas with limited resources. One strategy that has emerged to address treatment gaps is to rely on nonspecialists (eg, lay health workers, teachers, social workers, and peer mentors) to provide mental health services. Although this approach can be effective, current strategies demand substantial training and supervision.1 They also require highly standardized interventions, which may paradoxically limit more person-centered treatments.2 Concurrently, the field of artificial intelligence (AI) is evolving rapidly and changing how we detect and treat mental health disorders. Artificial intelligence applications in psychiatry are varied and include developing prediction models for disease detection and prognosis, creating algorithms that can help clinicians choose the right treatment plan, monitoring patient progress based on data from wearable devices, building chatbots that deliver more personalized and timely interventions, and using AI techniques to analyze therapy session transcripts to improve treatment fidelity and quality.3-5

Opportunities and risks of large language models in psychiatry

The integration of large language models (LLMs) into mental healthcare and research heralds a potentially transformative shift, one offering enhanced access to care, efficient data collection, and innovative therapeutic tools. This paper reviews the development, function, and burgeoning use of LLMs in psychiatry, highlighting their potential to enhance mental healthcare through improved diagnostic accuracy, personalized care, and streamlined administrative processes. It is also acknowledged that LLMs introduce challenges related to computational demands, potential for misinterpretation, and ethical concerns, necessitating the development of pragmatic frameworks to ensure their safe deployment. We explore both the promise of LLMs in enriching psychiatric care and research through examples such as predictive analytics and therapy chatbots and risks including labor substitution, privacy concerns, and the necessity for responsible AI practices. We conclude by advocating for processes to develop responsible guardrails, including red-teaming, multi-stakeholder-oriented safety, and ethical guidelines/frameworks, to mitigate risks and harness the full potential of LLMs for advancing mental health.

What is Mental Health?

Mental health refers to the state of our mind, rather than our physical health. Just like feeling physically light or energized, when your mind feels light, calm, and motivated, it's a sign of good mental health. However, everyone experiences moments of sadness or feeling down, and it’s common to feel stress in daily life. While occasional low moods and stress are natural, prolonged periods of these feelings can lead to mental health issues. Mental health challenges can be hard for others to notice, and sometimes difficult to express, which can delay recovery.

World Mental Health Day 2024: A Focus on Connection

The 2024 World Mental Health Day highlights the importance of human connection for mental well-being. The event, themed "Connecting—Anywhere, to Anyone," aims to build a supportive network for mental health.

Anyone Can Experience Mental Health Issues

Mental health conditions are becoming more common, with estimates that 1 in 5 people will experience a mental health disorder at some point in their lives. These conditions don’t only affect certain people; they can occur in anyone as a result of accumulated stress. Symptoms, such as persistent insomnia or feelings of sadness, vary from person to person. If you or someone you know is struggling, it’s important to reach out to family, friends, or a professional for support. If it’s difficult to speak with someone close or if no one is available, there are mental health helplines where you can share your concerns and get help.

Diagnosing Mental Health Conditions

Mental health conditions vary widely in their types and symptoms. Diagnosing them is different from diagnosing physical illnesses. While physical diseases are often classified by affected organs or causes, mental illnesses generally focus on the brain. Many mental conditions lack known causes, so current diagnostic methods look at key symptoms, the duration of those symptoms, and how much they affect daily life. The Diagnostic and Statistical Manual of Mental Disorders (DSM) from the American Psychiatric Association and the International Classification of Diseases (ICD) from the World Health Organization are widely used for mental health diagnosis, even in Japan. In these systems, diagnosis is usually not based on the cause of the condition, but rather on the symptoms and their impact.

Treatment and Support for Mental Health

If you’re concerned about your mental health, don’t hesitate to seek support from family, friends, professionals, or mental health services. Sometimes, talking with those close to you can be difficult, but public mental health services can also offer support. They may provide information on various support systems for mental health treatment and daily life assistance.

Building a Society that Values Mental Health

To create a society where everyone can live peacefully and authentically, we must prioritize mental health for all. This includes working with local governments and communities to build an inclusive society free of discrimination and prejudice, where everyone can thrive. It’s also essential to move beyond the dynamic of "those who support" and "those who are supported" to create a society where mutual help is the norm.

Japan's Ministry of Health, Labour, and Welfare is advancing the creation of a "Comprehensive Community Care System for Mental Health." This system ensures that people can live safely, regardless of whether they have a mental disorder, by providing comprehensive care that includes medical treatment, disability and welfare support, housing, employment, community help, and education.

Comprehensive Community Care for Mental Health

Community support is key to building a comprehensive care system for mental health. Raising public awareness and educating residents on how to provide initial support is essential. The Ministry of Health is promoting a "Mental Supporter" training program, which teaches people how to listen and support those around them, such as family members or coworkers. This program is open to people of all ages, from children to the elderly.

By valuing mental health, we can work together to create a society where everyone feels supported and able to live authentically.

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Technical Advisor to the R&D Team

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Geoffrey Hinton

The world's largest online destination for mental health and behavioral science.

Accessible mental health support for everyone, anytime. Whether you're seeking help, supporting a team, or delivering care— is here to help.  is an AI-based mental health platform that provides anonymous, safe, and  support for everyday mental health challenges.

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"Collaborative Research Team"

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has developed tests and assessments available in many languages, which have been completed over one million times. Our research combines psychological studies, workplace experience, and advanced data technology. These assessments help individuals understand themselves and others better, improving communication and teamwork. They provide valuable insights for managers to make better hiring and training decisions. Overall, our tools enhance personal growth, streamline processes, and build stronger, more effective teams in the workplace.

We use sophisticated psychometrics, combined with human expertise to gather high-quality data about people, teams, and workplaces. Psychometric assessments provide detailed insights into individual behaviours, strengths, and areas for development. We can use this data to uncover patterns and trends that offer a deep understanding of how teams function and how workplace dynamics influence performance. Using the right research and assessment processes we can answer nearly any question about people at work.

Analysing, sharing, and discussing this data equips everyone in the organisation to understand the same key issues from the same perspective. This shared understanding creates a common language across all levels of the organisation. It helps align individual and team goals with organisational objectives. Ultimately, this approach drives better decision-making, stronger team dynamics, and overall workplace success.

Results are generated using millions of data points collected from over 50 countries around the world across more than a decade of research.

If you would like customized testing or psychometric development services for any workplace applications, get in touch!

Learn how to use psychometrics and psychological expertise to boost well-being, improve productivity and performance.

Develop yourself, your team or your organization through interactive expert discussions applying the latest psychological and scientific research.

Build better teams, tools and processes, guided by psychological expertise.

Bespoke psychometrics

 

Customized reports and assessments for job roles, levels of expertise, companies or industry sectors.

Benchmarking based on job, company, sector or national standards.

AI in mental health is a growing field that uses artificial intelligence to improve mental health care in several ways, including diagnosis, treatment, and support. Key areas of focus include:

1. Diagnosis and Screening

AI tools can analyze large amounts of data from patients' speech, behavior, and physiological responses to identify patterns associated with mental health conditions. For example, natural language processing (NLP) techniques are used to assess speech for signs of depression, anxiety, or other disorders, helping clinicians identify conditions earlier.

2. Personalized Treatment

AI can assist in creating personalized mental health treatment plans by analyzing patient data such as medical history, therapy outcomes, and genetic factors. This allows healthcare providers to offer more tailored treatment options, such as suggesting specific therapy types (e.g., cognitive-behavioral therapy or mindfulness) that align with the patient's unique needs.

3. Virtual Therapists and Chatbots

AI-based mental health chatbots provide 24/7 support for patients. These chatbots use cognitive-behavioral techniques and empathy to engage with users, helping them manage symptoms of anxiety, stress, or depression in real-time. While not a replacement for human therapists, they are valuable tools for providing immediate help.

4. Predicting Relapses and Crisis Management

AI systems are being developed to predict mental health relapses or crises by continuously monitoring patient data. Machine learning algorithms can identify patterns that suggest a patient is at risk of worsening mental health and alert clinicians or caregivers for timely intervention.

5. Data-Driven Research

AI can analyze massive datasets from clinical trials, patient records, or even social media to identify new mental health trends and treatments. This helps accelerate research in identifying novel therapeutic approaches or understanding how mental health disorders develop.

6. Reducing Stigma and Increasing Accessibility

AI tools have the potential to reduce the stigma associated with seeking mental health treatment by offering privacy and accessibility. These tools allow people to seek help without the fear of judgment and make mental health care more accessible, especially in areas with limited healthcare resources.

The use of AI in mental health is continually evolving, with advancements in machine learning and natural language processing offering more sophisticated and sensitive tools for diagnosing and treating mental health issues. However, ethical considerations, such as data privacy and the limitations of AI in providing empathetic care, are also critical topics in this domain.

 

What information could AI provide about mental health? Computational psychiatry has the potential to gain insight into any condition with a large enough dataset. Machine learning could identify which genes contribute to the development of autism or the factors that render adolescents vulnerable to binge-drinking such as brain size or parental divorce. These programs could reveal which systems are affected by dopamine in patients with Parkinson’s disease, or a person’s risk for depression based on factors such as sex and childhood trauma. Could AI help diagnose bipolar disorder? Artificial intelligence has the potential to leverage large datasets to improve diagnoses and reduce misdiagnoses. For example, depressive episodes in bipolar disorder and depression can be difficult to distinguish; many patients with bipolar are misdiagnosed with major depressive disorder. A machine learning algorithm that used self-reports and blood samples recently identified bipolar disorder patients in various scenarios, potentially providing a helpful supplement for clinicians in the future

 

Could AI help diagnose autism?

There are currently no medical tests to definitively diagnose autism, but a recent study demonstrated that a machine learning algorithm identified proteins that differed in boys with autism and that predicted the severity of the condition. As this technology continues to evolve, it could help diagnose autism based on biomarkers.

Could AI help diagnose schizophrenia?

A recent study found that a machine learning algorithm classified cases of schizophrenia based on brain images with 87 percent accuracy. The pattern-recognition skills and predictive abilities of AI could provide a valuable tool for clinicians diagnosing schizophrenia.

Can AI help treat mental illness?

It’s important to translate the fascinating discoveries of AI into applications that can really help people. This can involve pinning down predictive risk factors for psychiatric conditions: Are there specific brain regions that make people more likely to commit suicide? Or to become depressed? Are certain medications effective for some patients with schizophrenia but not others? Doctors can then assess patient risk and provide proactive, personalized mental healthcare.

Can AI accelerate drug discovery?

Artificial intelligence can analyze massive datasets for difficult-to-spot connections between drugs, diseases, and biological processes to identify potential treatments. For example, a machine learning framework recently predicted which of the 20,000 FDA-approved drugs had the greatest likelihood of helping to treat Alzheimer's disease.

The Ethics of Artificial Intelligence

 

The evolution of artificial intelligence has led to countless ethical questions. Will machine learning perpetuate bias and inequality? Will AI infringe on human privacy and freedoms? Will humans lose their jobs to robots? Will machines become more intelligent than humans?

People are right to question the nature of machines that can evolve on their own. By actively engaging with these concerns, hopefully humans can develop ethical systems of artificial intelligence moving forward.

Why does the technology sector pose unique ethical challenges?

People interact with technology on an unprecedented scale and in many different environments—at work, in the supermarket, in the car, at home. Technology deployers have some responsibility to keep people safe as AI poses ethical challenges. Whether it’s anticipating systemic bias, recognizing when technologies coerce decision-making, intercepting malicious actors who wish to weaponize platforms, or taking a stand on overzealous surveillance, creators and consumers need to make sure that technology serves the population well.

What are some underappreciated ethical concerns about AI?

One ethical concern about artificial intelligence is the potent yet subtle influence of technology on people’s choices and decision-making. Companies are able to use all of the information they store about people to their advantage—“nudging” people towards decisions that are predominantly in the company’s interests. Another concern may arise from the technologies that claim to be able to read and interpret human emotions. The idea of a product deceiving a child or vulnerable adult into believing it truly “understands them,” and thereby influencing them, is worrying.

How can people build ethical AI?

This goal requires a moral approach to building AI systems and a plan for making AI systems ethical themselves. For example, developers of self-driving cars should be considering their social consequences, including ensuring that the cars themselves are capable of making ethical decisions. Building ethical artificial intelligence involves addressing ethical questions (e.g. how to prevent mass unemployment) and ethical concerns (e.g. clarifying moral values) and then developing a plan that aims to satisfy human needs.

From GUI to LLM: Unlocking the Future of Human Thought

Just as Steve Jobs saw the GUI as a world-changing inflection point, we're now at an even more profound moment with LLMs.

Key points

  • LLMs struggle to fully grasp the deeply embedded trends and language patterns of internet culture.

  • Rickrolling, a classic internet prank, reveals the limitations of machine intelligence in understanding.

  • Machine intelligence mimics human communication but lacks true understanding, especially in humor.

  • We don't yet fully understand the impact of training machine intelligence on vast datasets of online language.

Language models playing internet pranks

Some trends and language patterns are so deeply embedded in the language and culture of the internet that even the most advanced language models struggle to fully extract or contextualize them.

Humor, especially the internet’s unique and eccentric styles of jokes can help to explain the difference between human and machine intelligence. Apparentlylarge language models have a Rickrolling problem.

Rickrolling is an internet prank that's been an internet joke for decades. It uses hyperlinks to create a surprise connection. A link that appears to direct you to a meta-analytic research article on humor styles may actually redirect you to Rick Astley's 1987 hit "Never Gonna Give You Up."

Rickrolling and internet humor

This bait-and-switch is one of the most enduring internet pranks. It plays with the way technology (a hyperlink) is embedded in language. But it also is a subtle warning. Clicking a hyperlink on the internet may take you somewhere you didn’t expect. Rickrolling is a light and humorous way to remind people not to take everything online at face value.

There have been instances of large language models inadvertently Rickrolling users or chatbots pulling off a prank on unsuspecting clients of company chatbots.

This reveals much about human intelligence as well as the way language models work.

The culture of the internet is a vast repository of language, context, and subtlety. Simple jokes and cues require both an understanding of context and social situations. Human intelligence has evolved to quickly pick up subtleties from limited and ambiguous information.

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Social functions of humor

Jokes and pranks convey information on multiple levels and serve social functions. On the surface, they can showcase the personality of the individual telling the joke, whether they’re easy-going, friendly, or mischievous. But jokes can accomplish other objectives: signaling levels of familiarity, setting boundaries for conversation topics, and even sending covert messages about power dynamics.

A joke that demeans a person or group often reveals more about the joke-teller’s social intentions and perceived status. Whereas a self-deprecating joke can cut through social divisions and formalities, serving as a way to defuse tension or bring people closer. In both cases, humor is a social tool—either to reinforce or challenge social hierarchies.

Rickrolling is a perfect example of how an internet joke can operate on multiple levels. While it’s often just a light-hearted prank, it also carries a subtle warning: On the internet, you can’t always trust what you see. This reflects a broader truth and warning about the online world: What you encounter might not always be what you expect.

Humor reveals layers of language and culture

But it’s also revealing about how our language shapes the internet. The web is a vast network of language connected into networks of relationships and meaning. A prank like Rickrolling is used so often that it is deeply embedded in the language and code of the internet.

Large language models trained on vast datasets harvested from the web cannot simply ignore these elements of digital culture. Rickrolling is so ubiquitous that it shows up in the output of large language models that have failed to decode the context or social meaning of the joke.

Human vs. machine intelligence

Neil Lawrence, DeepMind Professor of Machine Learning at the University of Cambridge, explains the difference between human and machine intelligence in his book The Atomic Human. Lawrence deliberately uses the term ‘machine intelligence’, not 'artificial intelligence,’ to emphasize that machine intelligence is fundamentally different from how humans process information.

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Human intelligence is not just about processing data; it’s deeply embodied and evolved to handle ambiguity, make quick decisions with limited information, and rely on situational cues and social context. Machine intelligence can process vast amounts of data extraordinarily quickly to mimic human outputs. Yet it lacks true understanding, embodied knowledge, or the capacity to focus on specific information based on subtle situational cues. That means human intelligence is uniquely suited for environments and situations where nuance and context are key. Humor is the perfect example.

This is why a machine might replicate a Rickroll without any real understanding of the joke. It’s a combination of trust, deception, and unexpected results that is so attractive to human intelligence.

A caution embedded in the context

Rickrolling is a harmless prank that hints at a greater potential security risk. The fact that this has been incorporated into machine intelligence is extraordinarily funny, but it is also a warning.

The culture and the language of the internet are now deeply embedded in everything that is produced and created from it. All the human elements of trust, deception, unintended consequences, and uncertain relationships are wired into the connections of the network.

References

Lawrence, N., Montgomery, J., Schölkopf, B. (2023). Machine Learning for Science: Mathematics at the Interface of Data-driven and Mechanistic Modelling. Oberwolfach Reports(2), 1453–1484

Lawrence, N. D. (2024). The Atomic Human: Understanding ourselves in the age of AI. Allen Lane.

Marsh, M. (2019). American jokes, pranks and humor. In S. J. Bronner (Ed.). The Oxford Handbook of American Folklore and Folklife Studies. Oxford University Press.

MacRae, I. (2024) Web of value: Understanding blockchain and web3’s intersection of technology, psychology and business. Alexandria Books.

Silberling, A. (2024). This founder had to train its AI not to Rickroll people. TechCrunch.

The Psychologist (British Psychological Society):
Why psychologists should care about blockchain data

People Management (CIPD): Blockchain is coming for HR tech – whether you like it or not

AI as a Mirror Into the Self

AI-powered conversations with large language models may transform self-discovery and psychological insight, unlocking deeper truths about your mind with each iteration.

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Mental health is integral to overall well-being, impacting human ability to deal with challenges in life. Machine learning and AI hold promise in predicting mental illnesses by analysing behavioural patterns, aiding in early detection and intervention. This proactive approach can mitigate symptom escalation, improving mental health outcomes. This research study introduces a novel predictive framework integrating ensemble learning techniques and large language models (LLMs). Initially, ensemble learning, including AdaBoost, voting, and bagging, constructs a robust model with Random Forest emerging as optimal. Subsequently, a Large Language Model (LLM) enhances the pipeline. User input triggers mental health prediction by Random Forest, forwarded to a Google Gemini model via an API key, generating personalised insights, marking a significant advancement in mental health prediction.Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack of objective outcomes and fidelity metrics. AI technologies and specifically Natural Language Processing (NLP) have emerged as tools to study mental health interventions (MHI) at the level of their constituent conversations. However, NLP’s potential to address clinical and research challenges remains unclear. We therefore conducted a pre-registered systematic review of NLP-MHI studies using PRISMA guidelines (osf.io/s52jh) to evaluate their models, clinical applications, and to identify biases and gaps. Candidate studies (n = 19,756), including peer-reviewed AI conference manuscripts, were collected up to January 2023 through PubMed, PsycINFO, Scopus, Google Scholar, and ArXiv. A total of 102 articles were included to investigate their computational characteristics (NLP algorithms, audio features, machine learning pipelines, outcome metrics), clinical characteristics (clinical ground truths, study samples, clinical focus), and limitations. Results indicate a rapid growth of NLP MHI studies since 2019, characterized by increased sample sizes and use of large language models. Digital health platforms were the largest providers of MHI data. Ground truth for supervised learning models was based on clinician ratings (n = 31), patient self-report (n = 29) and annotations by raters (n = 26). Text-based features contributed more to model accuracy than audio markers. Patients’ clinical presentation (n = 34), response to intervention (n = 11), intervention monitoring (n = 20), providers’ characteristics (n = 12), relational dynamics (n = 14), and data preparation (n = 4) were commonly investigated clinical categories. Limitations of reviewed studies included lack of linguistic diversity, limited reproducibility, and population bias. A research framework is developed and validated (NLPxMHI) to assist computational and clinical researchers in addressing the remaining gaps in applying NLP to MHI, with the goal of improving clinical utility, data access, and fairness.
Methods
Search protocol and eligibility The systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The review was pre-registered, its protocol published with the Open Science Framework (osf.io/s52jh). The review focused on NLP for human-to-human Mental Health Interventions (MHI), defined as psychosocial, behavioral, and pharmacological interventions aimed at improving and/or assessing mental health (e.g., psychotherapy, patient assessment, psychiatric treatment, crisis counseling, etc.). We excluded studies focused solely on human-computer MHI (i.e., conversational agents, chatbots) given lingering questions related to their quality [38] and acceptability [42] relative to human providers. We also excluded social media and medical record studies as they do not directly focus on intervention data, despite offering important auxiliary avenues to study MHI. Studies were systematically searched, screened, and selected for inclusion through the Pubmed, PsycINFO, and Scopus databases. In addition, a search of peer-reviewed AI conferences (e.g., Association for Computational Linguistics, NeurIPS, Empirical Methods in NLP, etc.) was conducted through ArXiv and Google Scholar. The search was first performed on August 1, 2021, and then updated with a second search on January 8, 2023. Additional manuscripts were manually included during the review process based on reviewers’ suggestions, if aligning with MHI broadly defined (e.g., clinical diagnostics) and meeting study eligibility. Search string queries are detailed in the supplementary materials. Eligibility and selection of articles To be included, an article must have met five criteria: (1) be an original empirical study; (2) written in English; (3) vetted through peer-review; (4) focused on MHI; and (5) analyzed text data that was gathered from MHI (e.g., transcripts, message logs). Several exclusion criteria were also defined: (a) study of human-computer interventions; (b) text-based data not derived from human-to-human interactions (i.e., medical records, clinician notes); (c) social media platform content (e.g., Reddit); (d) population other than adults (18+); (e) did not analyze data using NLP; or (f) was a book chapter, editorial article, or commentary. Candidate manuscripts were evaluated against the inclusion and exclusion criteria initially based on their abstract and then on the full-text independently by two authors (JMZ and MM), who also assessed study focus and extracted data from the full text. Disagreement on the inclusion of an article or its clinical categorization was discussed with all the authors following full-text review. When more than one publication by the same authors used the same study aim and dataset, only the study with the most technical information and advanced model was included, with others classified as a duplicate and removed. Reasons for exclusion were recorded.

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Analyzing mental changes using AI.

Utilizing "skin analysis" technology

When fatigue and stress accumulate, various changes occur in the skin, such as breakouts or blemishes. Previously, cosmetic companies approached research and development with the goal of "relieving stress to improve skin." However, this time, they took the opposite approach—detecting stress levels based on skin conditions. Leveraging a database of skin and fatigue data from approximately 2,000 individuals, they developed technology that estimates stress and fatigue levels from the surface condition of the skin.

The company also focused on changes in complexion. By examining the constant color changes in the face caused by blood flow, they can determine heart rate and heart rate variability. Heart rate variability is regulated by the autonomic nervous system, and with a smartphone camera, they can detect these subtle movements to estimate the state of the autonomic nervous system. In addition to objective data, they also incorporate subjective data—how people feel when they look at their own face—to assess the accumulation of stress and fatigue.

Focusing on cognitive behavioral therapy.

An app that incorporates Cognitive Behavioral Therapy (CBT), a type of psychotherapy, to help users maintain balanced thinking without feeling overwhelmed by stress. A program focused on developing skills to cope with depressed moods, as well as the development of programs tailored to high-demand themes such as "anxiety" and "insomnia."

Mental health refers to the overall well-being of one's mind, emotions, and behavior, and is essential for a balanced and fulfilling life.

According to the World HealthOrganization (WHO), health is not just the absence of illness but a state of complete physical, mental, and social well-being. Therefore, mental health focuses on promoting and maintaining this well-being.

Purpose of Mental Health Care

Mental health care is vital as anyone can experience mental health issues, just like physical health problems. Mental health care is needed in various settings, such as communities, homes, and workplaces. In particular, workplaces have seen increased focus on mental health due to rising stress and fatigue in modern labor environments. For instance, Japan’s Ministry of Health, Labor, and Welfare established the “Mental Health Care Guidelines” in 2008 to promote workplace mental health.

What Does "Being Mentally Healthy" Mean?

Mental health is closely related to physical health, and the Ministry of Health, Labor, and Welfare in Japan identifies four key aspects of mental well-being:

  • Emotional health: The ability to recognize and express one’s emotions.

  • Intellectual health: The capacity to think rationally and solve problems effectively.

  • Social health: Building constructive and positive relationships with others.

  • Personal health: Finding purpose in life and actively making choices.

When these aspects are in harmony, a person is considered mentally healthy, and it greatly affects their quality of life.

The Importance of Rest for Mental Health

Mental health is influenced by various factors such as physical condition, socioeconomic status, and relationships. Physical health and mental well-being are interrelated. Proper rest, including sufficient sleep and managing stress, is essential for maintaining mental health. Regular exercise, balanced nutrition, and adequate relaxation are critical components of mental well-being.

Types of Mental Health Issues

Modern society is often described as highly stressful, contributing to mental health problems such as depression, schizophrenia, bipolar disorder, personality disorders, addiction, and cognitive impairments. Depression, for example, affects around 3-5% of the global population. Early detection and treatment are crucial for preventing serious consequences, including suicide.

Self-Check for Mental Health

Self-monitoring is essential for maintaining mental health. Regularly checking for changes in mood, energy, concentration, and overall well-being helps in early identification of mental health issues. Signs to watch for include fatigue, irritability, inability to focus, and physical symptoms like headaches or sleep problems.

Mental Health Care in the Workplace

Workplaces play a significant role in mental health through "line care," where supervisors monitor and support the mental well-being of their team. Managers are responsible for fostering a supportive environment where employees can share their concerns and seek help when needed. Training and resources for supervisors are essential to effectively manage their teams' mental health.

In conclusion, mental health care is important across all aspects of life—personal, professional, and societal. Regular self-checks and being mindful of both physical and mental changes are key to maintaining overall health. When necessary, seeking support from healthcare providers or mental health professionals is essential to address any concerns.

Mental health is an essential part of people’s lives and society. Poor mental health affects our well-being, our ability to work, and our relationships with friends, family, and community. Mental health conditions are not uncommon. Hundreds of millions suffer from them yearly, and many more do over their lifetimes. It’s estimated that 1 in 3 women and 1 in 5 men will experience major depression in their lives. Other conditions, such as schizophrenia and bipolar disorder, are less common but still have a large impact on people’s lives. Mental illnesses are treatable, and the impact they have can be reduced. Despite this, treatment is often lacking or poor quality, and many feel uncomfortable sharing their symptoms with healthcare professionals or people they know. This also makes it difficult to estimate the actual prevalence of mental illnesses. To support them, it’s essential to have good data to understand these conditions – how, when, and why they occur, how many people are affected by them, and how they can be treated effectively and safely. On this page, we show data on the prevalence of mental illnesses and their burden, and people’s attitudes toward mental health.

Get closer to nature We all live with worries and fears, uncertainties, losses that leave us sad and pressures that make us feel stressed. Nature can have a really calming effect on us. We have been living around other animals and plants for thousands of years. Some people say nature helps them feel calmer, more hopeful and less alone. Our research found that going for a walk was UK adults’ favourite way of coping with stress during the pandemic in 2020. In Japan, some people do “forest bathing”, which may improve their mental health. So going to a forest or wood and experiencing the different smells, sounds and textures may really help. To get the best out of nature’s healing effects, try tuning your senses to what’s around you – the trees, plants, birds and animals, for instance, and water such as ponds or the seashore. Take a deep breath and see how you feel. The idea is to get connected with your natural surroundings. Find out more about mental health and nature, including our guide Thriving With Nature, produced with wildlife charity WWF.

More tips

2. Get more from your sleep Anyone who has struggled with sleep will know what a difference it makes to our bodies, minds and ability to cope with life. We all have times when we sleep badly, and some of us live in situations that make a good night’s sleep impossible. For many people, sleep is often the first thing that suffers when we're struggling with our mental health. Adults need between 7 and 9 hours' sleep a night, including enough dreaming sleep and enough deeper sleep. If you’re struggling with your sleep, think about trying a few simple changes. For example: Develop a relaxing bedtime routine to help you start winding down before you actually go to sleep Avoid TV and mobile screens, alcohol and caffeine before bed. This will help you fall asleep and stay asleep. You could also avoid vigorous exercise before bed. Go to bed and get up at around the same time every day, including weekends. For more suggestions, try this guide to sleep and mental health. It includes information about potentially helpful apps and getting professional help if self-help isn’t working for you. More tips Load videoGet more from sleep: best mental health tips series Load videoKeep moving: best mental health tips series

3. Keep moving Our bodies and minds are connected, so looking after ourselves physically also helps us prevent problems with our mental health (it works the other way around, too). Moving our bodies – with sport, gardening, dancing, cycling, walking the dog, cleaning or going to the gym, for example – are great ways to improve our mental and physical health. Exercise releases “feel good” hormones that reduce feelings of stress and anger. It also helps us feel better about our bodies. It can improve our sleep too. If it involves other people, like being part of a team, a class or a group we see regularly, that can also boost our mental health. We don’t have to be professional athletes, or sporty, to get the benefits of being physically active. From walking to dancing and basketball to the gym, the best exercise is simply the one we enjoy. More tips

4. Eat healthy food Food and drink affect our bodies, brains and mood – for good or bad. Sugary snacks and drinks can give us a temporary “high” or sense of comfort that can feel irresistible. But they soon leave us feeling exhausted or jittery. Caffeine in coffee, tea, or so-called energy drinks can also have this effect. A balanced diet with lots of vegetables and fruit is essential for good physical and mental health. How we eat is also important. Having meals with other people can help to grow relationships – with family, friends, partners, and colleagues. This is really important in protecting everyone’s mental health and preventing problems. Food can also get mixed up with our feelings. Some people over-eat or under-eat when they are upset. It may help to talk to someone we trust and get professional support if we need it. More tips Load videoEat healthy food: best mental health tips series Load videoBe kind: best mental health tips series

 5. Be kind and help create a better world If you have ever felt lonely, you will know how much it helps to have even a small connection with someone else. Exchanging a smile or friendly words can be comforting and lift our mood. Research shows that being kind is good for both sides. Being kind can boost our mood, help us feel more capable, strengthen our connections with others and even make us cope better with stress. If you don’t know where to start, you could try small but meaningful acts of kindness, such as offering a smile or a few kind words to another person. You could make their day but also feel better for it - friendly connections with others are vital for our mental health. More inspiration about acts of kindness We can also help others by doing volunteer work and getting together with other people to work for good causes. These things can help us feel connected with others and the world around us. It can give us the sense that we can make a difference. All of this is good for our mental health. More tips

6. Be curious and open-minded to new experiences We can all get stuck in familiar ways, like how we spend our time and what we think about ourselves and the world. This can be self-fulfilling, with our expectations influencing what actually happens, for good or bad. For example, we might say to ourselves: “Things never get better” or “I’m useless” as a response to what others have said to us. It can help to notice these thoughts and try out new ones, such as “I can change things for the better” and “there is so much I can do”. Life can feel more interesting, lively and rewarding when we are open to trying new experiences and experimenting with how we do things. It could be as simple as what we have for breakfast or the route we walk with the dog. It could also need some planning, like an adventure holiday. We might find a new place that it turns out we love, discover a talent we didn’t know we had or meet someone new and important. Load videoBe curious: best mental health tips series Load videoPlan things to look forward to: best mental health tips series

 7. Plan things to look forward to Life throws all sorts at us, and during hard times, we may feel hopeless and not have the energy to plan for the future. Things to look forward to, including fun activities, can help us cope with difficult situations. Making plans for things we enjoy can increase our sense of hope, which is important for our mental health. Our plan could be for anything from small pleasures, like a cup of tea or your favourite TV programme or dance class, to a trip with family or friends, or seeing your favourite film, sports team or singer. Whether it’s a small or a big one, the important thing is to plan it. Decide what you’ll do, when and with who and, if needed, book it. It’s important to follow your plan – and repeat.

8. Learn to understand and manage your feelings Feeling very upset can interfere with our lives, making it hard to think clearly or work, relax, study, sleep or deal with other people. Many of us will know when we’re upset but not be sure what we’re feeling. Is it sadness, fear, shame, loneliness, anger or something else? We don’t always know why we’re feeling that way. It often helps to give our feelings our attention without judging them, without telling ourselves we’re stupid or weak to feel the way we do. It may seem strange and uncomfortable to do this. But practice and patience with ourselves will help. Naming what we’re feeling is also likely to help. So, we can say to ourselves: “I’m feeling really irritable today but also sad.” Another helpful step can be to work out what has led to us feeling this way – might it be a disagreement or disappointment that you’ve had? Some women might find that their periods sometimes (or always) affect their moods. Talking kindly to ourselves, in the same way, we might reassure a small child we care about, is also important. It can be very comforting. You might feel uncomfortable initially, but give it a go – it might just help. Some people feel better if they write down their feelings in a notebook or on their phone. You might want to think about how to protect your privacy before doing this. You could try repeating something positive about yourself a few times each day. (This is sometimes called a “mantra”. “I am on a journey, growing and developing”, for example.) Research shows this reduces negative thoughts and feelings. Some people also feel that an activity called “mindfulness” is helpful. Talking with someone else can also be helpful – and that’s our next suggestion…

9. Talk to someone you trust for support Many of us have learned to bottle things up inside us and try to ignore painful feelings. It can take a lot of courage to tell someone else how we’re feeling or what we’re finding hard, especially if we don’t usually do that kind of thing. Just talking things through with a person we trust can help and feel like a relief. Use your own words. It’ll make you feel safer and less alone, and that will help protect your mental health and prevent problems. Talking may also change how you see and feel about the situation in ways you find helpful. Another possible benefit is that talking may strengthen your relationship with the person you speak with. This will benefit both of you and make it easier for them to turn to you when they need support themselves. Information about talking with friends about your mental health Information about talking with a health professional about how you’re feeling

10. Be aware of using drugs and/or alcohol to cope with difficult feelings Many of us sometimes use drugs to block out “difficult” feelings such as sadness, fear or shame. No one wants to feel bad; for some people, drugs and/or alcohol offer temporary relief. Unfortunately, they don’t stop the feelings from returning and may make things worse or create other problems, including damage to mental and physical health, relationships, work, or study. If you think you may be using drugs or alcohol to cope with difficult feelings, it may help to notice it without beating yourself up about it. Being understanding and kind to yourself is good for your mental health. A possible next step could be to talk with someone you trust or one of the charities that offer confidential, free information and advice, including how to reduce the harmfulness of using drugs. Have a look at Talk To Frank, for example. You could also look for other ways of coping with painful feelings, such as getting help with the situation causing them, if possible, and finding a trusted person to talk with. This could be a friend or relative, a colleague, a person working for a charity helpline, your GP or a counsellor. Many charities around the country offer low-cost or free therapy. Find more detailed information about getting help with your mental health from many potential sources. Some people find it helps to go to “anonymous” meetings, such as Narcotics Anonymous, to share with others who have/had similar experiences. Some of these meetings are also now online.

11. Try to make the most of your money and get help with problem debts Money problems can be unavoidable, and we may have no choice about being in debt. Fears about paying debts, bills and essentials such as food and electricity can be very stressful. If we feel responsible for sending money back home or are the only earner in our household, we may feel burdened and isolated. Feelings like this can make it harder to cope with everything, including the money problems themselves. There are things we can do to help prevent financial problems causing issues with our mental health. We might feel ashamed of what we’re going through, which may make it harder to tell anyone, leaving us feeling alone with it. The truth is, we’re never alone with money problems. Research shows they are one of the most common and serious sources of stress for many people. Even if our finances are impossible to change, sharing our fears with another person who’s not involved and who you trust is likely to help us feel less alone and overwhelmed. This may help us to see new ways forward. If we are struggling financially, including with debt, it can be helpful to talk about it with an expert at a charity that offers free money advice. They are very experienced with what can go wrong with people’s finances and what may help. It can be really helpful to ask for help before any debt becomes unmanageable. But getting help will benefit everyone. Here is a list of organisations that offer free advice about financial problems, created by the charity Money and Mental Health. Talking with one of these free, expert advisers could also lead to you finding out about new possibilities, such as benefits you’re entitled to, your legal rights, ways to reduce the size of your debt repayments and other ways to save money.

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