Artificial intelligence in mental health

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Artificial intelligence (AI) in mental health refers to the use of AI, computational technologies and algorithms to supplement the understanding, diagnosis, and treatment of mental health disorders. [1]

Contents

Background

In 2019, 1 in every 8 people, or 970 million people around the world were living with a mental disorder, with anxiety and depressive disorders the most common. [2] In 2020, the number of people living with anxiety and depressive disorders rose significantly because of the COVID-19 pandemic. [3] Additionally, the prevalence of mental health and addiction disorders exhibits a nearly equal distribution across genders, emphasizing the widespread nature of the issue. [4]

The use of AI in mental health aims to support responsive and sustainable interventions against the global challenge posed by mental health disorders. Some issues common to the mental health industry are provider shortages, inefficient diagnoses, and ineffective treatments. The AI industry sees a market in healthcare, with a focus on mental health applications, which are projected to grow substantially, from $5 billion in 2020 to an estimated $45 billion by 2026. This growth indicates a growing interest in AI's ability to address critical challenges in mental healthcare provision through the development and implementation of innovative solutions. [5]

Types of AI in mental health

As of 2020, there was no Food and Drug Administration (FDA) approval for AI in the field of Psychiatry. [6] There are two components of AI that are currently widely available for multiple applications, they are Machine learning (ML) and Natural language processing (NLP).

Machine learning

Machine learning is a way for a computer to learn from large datasets presented to it, without explicit instructions. It requires structured databases; unlike scientific research which begins with a hypothesis, ML begins by looking at the data and finding its own hypothesis based on the patterns that it detects. [5] It then creates algorithms to be able to predict new information, based on the created algorithm and pattern that it was able to generate from the original dataset. [5] This model of AI is data driven, as it requires a huge amount of structured data—an obstacle in the field of psychiatry—with a lot of its patient encounters being based on interview and storytelling on the part of the patient. [5] Due to these limitations, some researchers have adopted a different method of developing ML models, a process named transfer learning, to be used in psychiatry based on trained models from different fields. [5]

Transfer learning was used by researchers to develop a modified algorithm to detect alcoholism vs. non-alcoholism, and on another occasion, the same method was used to detect the signs of post-traumatic stress disorder. [7] [8]

Natural language processing

One of the obstacles for AI is finding or creating an organized dataset to train and develop a useful algorithm. Natural language processing can be used to create such a dataset. NLP is a way for a computer to analyze text and speech, process semantic and lexical representations, as well as recognize speech and optical characters in data. This is crucial because many of the diagnoses and DSM-5 mental health disorders are diagnosed via speech in doctor-patient interviews, utilizing the clinician's skill for behavioral pattern recognition and translating it into medically relevant information to be documented and used for diagnoses. NLP can be used to extract, organize, and structure data from patients' everyday interactions, not just during a clinical visit, raises ethical and legal concerns over consent to personal data use and data anonymization. [9]

Applications

Diagnosis

AI with the use of NLP and ML can be used to help diagnose individuals with mental health disorders. It can be used to differentiate closely similar disorders based on their initial presentation to inform timely treatment before disease progression. For example, it may be able to differentiate unipolar from bipolar depression by analyzing imaging and medical scans. [5] AI also has the potential to identify novel diseases that were overlooked due to the heterogeneity of presentation of a single disorder. [5] Doctors may overlook the presentation of a disorder because while many people get diagnosed with depression, that depression may take on different forms and be enacted in different behaviors. AI can parse through the variability found in human expression data and potentially identify different types of depression.

Prognosis

AI can be used to create accurate predictions for disease progression once diagnosed. [5] AI algorithms can also use data-driven approaches to build new clinical risk prediction models [10] without relying primarily on current theories of psychopathology. However, internal and external validation of an AI algorithm is essential for its clinical utility. [5] In fact, some studies have used neuroimaging, electronic health records, genetic data, and speech data to predict how depression would present in patients, their risk for suicidality or substance abuse, or functional outcomes. [5]

Treatment

In psychiatry, in many cases multiple drugs are trialed with the patients until the correct combination or regimen is reached to effectively treat their ailment—AI could theoretically be used to predict treatment response based on observed data collected from various sources. This use of AI could bypass all the time, effort, resources needed, and burden placed on both patients and clinicians. [5]

Benefits

AI in mental health offers several benefits, such as:

Mental health tech startups continue to lead investment activity in digital health despite the ongoing impacts of macroeconomic factors like inflation, supply chain disruptions, and interest rates. [12]

According to CB Insights, State of Mental Health Tech 2021 Report, mental health tech companies raised $5.5 billion worldwide (324 deals), a 139% increase from the previous year that recorded 258 deals.

A number of startups that are using AI in mental healthcare have closed notable deals in 2022 as well. Among them is the AI chatbot Wysa (20$ million in funding), BlueSkeye that is working on improving early diagnosis (£3.4 million), the Upheal smart notebook for mental health professionals (€1.068 million), and the AI-based mental health companion clare&me (€1 million).

An analysis of the investment landscape and ongoing research suggests that we are likely to see the emergence of more emotionally intelligent AI bots and new mental health applications driven by AI prediction and detection capabilities.

For instance, researchers at Vanderbilt University Medical Center in Tennessee, US, have developed an ML algorithm that uses a person’s hospital admission data, including age, gender, and past medical diagnoses, to make an 80% accurate prediction of whether this individual is likely to take their own life. [13] And researchers at the University of Florida are about to test their new AI platform aimed at making an accurate diagnosis in patients with early Parkinson’s disease. [14] Research is also underway to develop a tool combining explainable AI and deep learning to prescribe personalized treatment plans for children with schizophrenia. [15]

Criticism

AI in mental health is still an emerging field and there are still some concerns and criticisms about the use of AI in this area, such as:

See also

Related Research Articles

<i>Diagnostic and Statistical Manual of Mental Disorders</i> American psychiatric classification

The Diagnostic and Statistical Manual of Mental Disorders is a publication by the American Psychiatric Association (APA) for the classification of mental disorders using a common language and standard criteria. It is the main book for the diagnosis and treatment of mental disorders in the United States and Australia, while in other countries it may be used in conjunction with other documents. The DSM-5 is considered one of the principal guides of psychiatry, along with the International Classification of Diseases (ICD), Chinese Classification of Mental Disorders (CCMD), and the Psychodynamic Diagnostic Manual. However, not all providers rely on the DSM-5 as a guide, since the ICD's mental disorder diagnoses are used around the world and scientific studies often measure changes in symptom scale scores rather than changes in DSM-5 criteria to determine the real-world effects of mental health interventions.

A mental disorder, also referred to as a mental illness, a mental health condition, or a psychiatric disorder, is a behavioral or mental pattern that causes significant distress or impairment of personal functioning. A mental disorder is also characterized by a clinically significant disturbance in an individual's cognition, emotional regulation, or behavior, often in a social context. Such disturbances may occur as single episodes, may be persistent, or may be relapsing–remitting. There are many different types of mental disorders, with signs and symptoms that vary widely between specific disorders. A mental disorder is one aspect of mental health.

<span class="mw-page-title-main">Attention deficit hyperactivity disorder</span> Neurodevelopmental disorder

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterised by executive dysfunction occasioning symptoms of inattention, hyperactivity, impulsivity and emotional dysregulation that are excessive and pervasive, impairing in multiple contexts, and otherwise age-inappropriate.

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance.

Schizoaffective disorder is a mental disorder characterized by abnormal thought processes and an unstable mood. This diagnosis requires symptoms of both schizophrenia and a mood disorder: either bipolar disorder or depression. The main criterion is the presence of psychotic symptoms for at least two weeks without any mood symptoms. Schizoaffective disorder can often be misdiagnosed when the correct diagnosis may be psychotic depression, bipolar I disorder, schizophreniform disorder, or schizophrenia. This is a problem as treatment and prognosis differ greatly for most of these diagnoses.

Adjustment disorder is a maladaptive response to a psychosocial stressor. It is classified as a mental disorder. The maladaptive response usually involves otherwise normal emotional and behavioral reactions that manifest more intensely than usual, causing marked distress, preoccupation with the stressor and its consequences, and functional impairment.

A clinical decision support system (CDSS) is a health information technology that provides clinicians, staff, patients, and other individuals with knowledge and person-specific information to help health and health care. CDSS encompasses a variety of tools to enhance decision-making in the clinical workflow. These tools include computerized alerts and reminders to care providers and patients, clinical guidelines, condition-specific order sets, focused patient data reports and summaries, documentation templates, diagnostic support, and contextually relevant reference information, among other tools. CDSSs constitute a major topic in artificial intelligence in medicine.

<span class="mw-page-title-main">Personalized medicine</span> Medical model that tailors medical practices to the individual patient

Personalized medicine, also referred to as precision medicine, is a medical model that separates people into different groups—with medical decisions, practices, interventions and/or products being tailored to the individual patient based on their predicted response or risk of disease. The terms personalized medicine, precision medicine, stratified medicine and P4 medicine are used interchangeably to describe this concept, though some authors and organizations differentiate between these expressions based on particular nuances. P4 is short for "predictive, preventive, personalised and participatory".

Self-diagnosis is the process of diagnosing, or identifying, medical conditions in oneself. It may be assisted by medical dictionaries, books, resources on the Internet, past personal experiences, or recognizing symptoms or medical signs of a condition that a family member previously had or currently has.

Disease Informatics (also infectious disease informatics) studies the knowledge production, sharing, modeling, and management of infectious diseases. It became a more studied field as a by-product of the rapid increases in the amount of biomedical and clinical data widely available, and to meet the demands for useful data analyses of such data.

Child and adolescent psychiatry is a branch of psychiatry that focuses on the diagnosis, treatment, and prevention of mental disorders in children, adolescents, and their families. It investigates the biopsychosocial factors that influence the development and course of psychiatric disorders and treatment responses to various interventions. Child and adolescent psychiatrists primarily use psychotherapy and/or medication to treat mental disorders in the pediatric population.

Imaging informatics, also known as radiology informatics or medical imaging informatics, is a subspecialty of biomedical informatics that aims to improve the efficiency, accuracy, usability and reliability of medical imaging services within the healthcare enterprise. It is devoted to the study of how information about and contained within medical images is retrieved, analyzed, enhanced, and exchanged throughout the medical enterprise.

<span class="mw-page-title-main">Psychiatry</span> Branch of medicine devoted to mental disorders

Psychiatry is the medical specialty devoted to the diagnosis, prevention, and treatment of deleterious mental conditions. These include various matters related to mood, behaviour, cognition, perceptions, and emotions.

<span class="mw-page-title-main">Medical diagnosis</span> Process to identify a disease or disorder

Medical diagnosis is the process of determining which disease or condition explains a person's symptoms and signs. It is most often referred to as a diagnosis with the medical context being implicit. The information required for a diagnosis is typically collected from a history and physical examination of the person seeking medical care. Often, one or more diagnostic procedures, such as medical tests, are also done during the process. Sometimes the posthumous diagnosis is considered a kind of medical diagnosis.

The use of electronic and communication technologies as a therapeutic aid to healthcare practices is commonly referred to as telemedicine or eHealth. The use of such technologies as a supplement to mainstream therapies for mental disorders is an emerging mental health treatment field which, it is argued, could improve the accessibility, effectiveness and affordability of mental health care. Mental health technologies used by professionals as an adjunct to mainstream clinical practices include email, SMS, virtual reality, computer programs, blogs, social networks, the telephone, video conferencing, computer games, instant messaging and podcasts.

Psychiatry is, and has historically been, viewed as controversial by those under its care, as well as sociologists and psychiatrists themselves. There are a variety of reasons cited for this controversy, including the subjectivity of diagnosis, the use of diagnosis and treatment for social and political control including detaining citizens and treating them without consent, the side effects of treatments such as electroconvulsive therapy, antipsychotics and historical procedures like the lobotomy and other forms of psychosurgery or insulin shock therapy, and the history of racism within the profession in the United States.

<span class="mw-page-title-main">Artificial intelligence in healthcare</span> Overview of the use of artificial intelligence in healthcare

Artificial intelligence in healthcare is a term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to copy human cognition in the analysis, presentation, and understanding of complex medical and health care data, or to exceed human capabilities by providing new ways to diagnose, treat, or prevent disease. Specifically, AI is the ability of computer algorithms to arrive at approximate conclusions based solely on input data.

Explainable AI (XAI), often overlapping with Interpretable AI, or Explainable Machine Learning (XML), either refers to an artificial intelligence (AI) system over which it is possible for humans to retain intellectual oversight, or refers to the methods to achieve this. The main focus is usually on the reasoning behind the decisions or predictions made by the AI which are made more understandable and transparent. XAI counters the "black box" tendency of machine learning, where even the AI's designers cannot explain why it arrived at a specific decision.

<span class="mw-page-title-main">Merative</span> U.S. healthcare company

Merative L.P., formerly IBM Watson Health, is an American medical technology company that provides products and services that help clients facilitate medical research, clinical research, real world evidence, and healthcare services, through the use of artificial intelligence, data analytics, cloud computing, and other advanced information technology. Merative is owned by Francisco Partners, an American private equity firm headquartered in San Francisco, California. In 2022, IBM divested and spun-off their Watson Health division into Merative. As of 2023, it remains a standalone company headquartered in Ann Arbor with innovation centers in Hyderabad, Bengaluru, and Chennai.

<span class="mw-page-title-main">Artificial intelligence of things</span>

The Artificial Intelligence of Things (AIoT) is the combination of artificial intelligence (AI) technologies with the Internet of things (IoT) infrastructure to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics.

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