Artificial intelligence in mental health

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Artificial intelligence (AI) in mental health is the application of artificial intelligence (AI), computational technologies and algorithms to supplement the understanding, diagnosis, and treatment of mental health disorders. [1] AI is becoming a ubiquitous force in everyday life which can be seen through frequent operation of models like ChatGPT. [2]  Utilizing AI in the realm of mental health signifies a form of digital healthcare, in which, the goal is to increase accessibility in a world where mental health is becoming a growing concern. [3] Prospective ideas involving AI in mental health include identification and diagnosis of mental disorders, explication of electronic health records, creation of personalized treatment plans, and predictive analytics for suicide prevention. [3] [4]  Learning how to apply AI in healthcare proves to be a difficult task with many challenges, thus it remains rarely used as efforts to bridge gaps are deliberated. [3]

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. [5] In 2020, the number of people living with anxiety and depressive disorders rose significantly because of the COVID-19 pandemic. [6] Additionally, the prevalence of mental health and addiction disorders exhibits a nearly equal distribution across genders, emphasizing the widespread nature of the issue. [7]

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. [8]

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. [9] 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. [8] 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. [8] 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. [8] 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. [8]

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. [10] [11]

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. [12]

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. [8] AI also has the potential to identify novel diseases that were overlooked due to the heterogeneity of presentation of a single disorder. [8] 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. [8] AI algorithms can also use data-driven approaches to build new clinical risk prediction models [13] without relying primarily on current theories of psychopathology. However, internal and external validation of an AI algorithm is essential for its clinical utility. [8] 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. [8]

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. [8]

Benefits

AI in mental health offers several benefits, such as:

Challenges

AI in mental health also poses several challenges, including:

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. [15]

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. [16] 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. [17] Research is also underway to develop a tool combining explainable AI and deep learning to prescribe personalized treatment plans for children with schizophrenia. [18]

In January of 2024, Cedars-Sinai physician-scientists developed a first-of-its-kind program that uses immersive virtual reality and generative artificial intelligence to provide mental health support. The program is called XAIA which employs a large language model programmed to resemble a human therapist.

The University of Southern California is researching the effectiveness of a virtual therapist named Ellie. Through a webcam and microphone, this AI is able to process and analyze the emotional cues derived from the patient's face and the variation in expressions and tone of voice.

A team of Stanford Psychologists and AI experts created "Woebot". Woebot is an app that makes therapy sessions available 24/7. WoeBot tracks its users' mood through brief daily chat conversations and offers curated videos or word games to assist users in managing their mental health. A Scandinavian team of software engineers and a clinical psychologist created "Heartfelt Services". Heartfelt Services is an application meant to simulate conventional talk therapy with an AI therapist. [19]

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:

Ethical Issues

Though there is a large deal of progression to be made, the incorporation of AI in mental heath emphasizes a necessity for legal and regulatory frameworks as advancements are made. [3] Constructing harmony amidst human engagement and AI is a difficult task, as there is a risk of healthcare becoming seemingly robotic and losing the humanness that has previously defined the field. [4] Furthermore, granting patients a feeling of security and safety is a priority considering AI's reliance on individual data to perform and respond to inputs. If not approached properly, the process of trying to increase accessibility could remove elements that negatively alter patient experience with receiving mental support. [4] To avoid veering in the wrong direction, more research should continue to develop a deeper understanding of where the incorporation of AI produces advantages and disadvantages. [2]

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.

<span class="mw-page-title-main">Chatbot</span> Program that simulates conversation

A chatbot is a software application or web interface that is designed to mimic human conversation through text or voice interactions. Modern chatbots are typically online and use generative artificial intelligence systems that are capable of maintaining a conversation with a user in natural language and simulating the way a human would behave as a conversational partner. Such chatbots often use deep learning and natural language processing, but simpler chatbots have existed for decades.

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, personalized and participatory".

Health technology is defined by the World Health Organization as the "application of organized knowledge and skills in the form of devices, medicines, vaccines, procedures, and systems developed to solve a health problem and improve quality of lives". This includes pharmaceuticals, devices, procedures, and organizational systems used in the healthcare industry, as well as computer-supported information systems. In the United States, these technologies involve standardized physical objects, as well as traditional and designed social means and methods to treat or care for patients.

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.

Artificial intelligence (AI) has been used in applications throughout industry and academia. Similar to electricity or computers, AI serves as a general-purpose technology that has numerous applications. Its applications span language translation, image recognition, decision-making, credit scoring, e-commerce and various other domains. AI which accommodates such technologies as machines being equipped perceive, understand, act and learning a scientific discipline.

<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 the application of 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.

<span class="mw-page-title-main">Ada Health</span> Health company based in Berlin

Ada Health was founded in 2011, through the collaboration of Dr. Claire Novorol, Professor Martin Hirsch, and Daniel Nathrath.

<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">Edward Y. Chang</span> American computer scientist

Edward Y. Chang is a computer scientist, academic, and author. He is an adjunct professor of Computer Science at Stanford University, and Visiting Chair Professor of Bioinformatics and Medical Engineering at Asia University, since 2019.

References

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