# Artificial intelligence in mental health

Artificial intelligence in mental health refers to the application of artificial intelligence (AI), computational technologies and algorithms to support the understanding, diagnosis, and treatment of mental health disorders. In the context of mental health, AI is considered a component of digital healthcare, with the objective of improving accessibility and addressing the growing prevalence of mental health concerns. Applications of AI in this field include the identification and diagnosis of mental disorders, analysis of electronic health records, development of personalized treatment plans, and analytics for suicide prevention. Despite its many potential benefits, the implementation of AI in mental healthcare presents significant challenges and ethical considerations, and its adoption remains limited as researchers and practitioners work to address existing barriers.

## 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 being the most common. In 2020, the number of people living with anxiety and depressive disorders rose significantly because of the COVID-19 pandemic. Additionally, the prevalence of mental health and addiction disorders exhibits a nearly equal distribution across genders, emphasizing the widespread nature of the issue.
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 Global market for AI-driven mental health applications is projected to grow significantly, with estimates suggesting an increase from 0.92 billion USD in 2023 to $14.89 billion USD by 2033. 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.

## AI-driven approaches

There are several components of AI that are currently widely available for multiple applications, such as machine learning (ML), natural language processing (NLP), deep learning (DL), and computer vision (CV). These technologies enable early detection of mental health conditions, personalized treatment recommendations, and real-time monitoring of patient well-being.

### Machine learning

Machine learning is an AI technique that enables computers to identify patterns in large datasets and make predictions based on those patterns. Unlike traditional medical research, which begins with a hypothesis, ML models analyze existing data to uncover correlations and develop predictive algorithms. ML in psychiatry is limited by data availability and quality. Many psychiatric diagnoses rely on subjective assessments, interviews, and behavioral observations, making structured data collection difficult. Researchers are addressing these challenges using transfer learning, a technique that adapts ML models trained in other fields for use in mental health applications.

### Natural language processing

Natural Language Processing allows AI systems to analyze and interpret human language, including speech, text, and tone of voice. In mental health, NLP is used to extract meaningful insights from conversations, clinical notes, and patient-reported symptoms. NLP can assess sentiment, speech patterns, and linguistic cues to detect signs of mental distress. 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. As research continues, NLP models must address ethical concerns related to patient privacy, consent, and potential biases in language interpretation.

### Deep Learning

Deep learning, a subset of ML, involves neural networks that mimic the human brain to analyze complex data. It is particularly useful for identifying subtle patterns in speech, imaging, and physiological data. Deep learning is used in neuroimaging analysis, helping researchers detect abnormalities in brain scans associated with conditions such as schizophrenia, depression, and PTSD. However, deep learning models require extensive, high-quality datasets to function effectively. The limited availability of large, diverse mental health datasets poses a challenge, as patient privacy regulations restrict access to medical records. Additionally, deep learning models often operate as "black boxes", meaning their decision-making processes are not easily interpretable by clinicians, raising concerns about transparency and clinical trust.

### Computer Vision

Computer vision enables AI to analyze visual data, such as facial expressions, body language, and micro expressions, to assess emotional and psychological states. This technology is increasingly used in mental health research to detect signs of depression, anxiety, and PTSD through facial analysis. CV can detect subtle nonverbal cues, such as hesitation or changes in eye contact, which may indicate emotional distress. Despite its potential, computer vision in mental health raises ethical and accuracy concerns. Facial recognition algorithms can be influenced by cultural and racial biases, leading to potential misinterpretations of emotional expressions. Additionally, concerns about informed consent and data privacy must be addressed before widespread clinical adoption.

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

### 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 application of AI has the potential to reduce the time, effort, and resources required while alleviating the burden on both patients and clinicians.

## Benefits

AI in mental health offers several benefits, such as:
* Improving the accuracy of diagnosis: AI-based systems can analyze data from various sources, such as brain imaging and genetic tests, to identify biomarkers of mental health conditions and improve the accuracy of diagnosis.
* Personalized treatment: AI-based systems can analyze data from electronic health records (EHRs), brain imaging, and genetic tests to identify the most effective treatment for specific individuals.
* Improving access to mental healthcare: AI-based systems can be used to deliver mental health interventions, such as cognitive behavioral therapy, in virtual environments, which can improve access to mental healthcare in areas where access is limited.
* Intelligent monitoring and early warning signs: AI-based systems can assist in recognition of mental health concerns earlier on, hence quicker turn overs in strategizing action plans and decreased chances of extreme episodes.
* Chatbots and virtual assistants: AI-based systems can accelerate the rate of customer care and boost overall efficiency through task features like appointment scheduling and organization of patient background information.
* Predictive analytics for suicide prevention: AI-based systems may be optimized to analyze data regarding suicide to locate trends to help better understand potential risks and probabilities in different groups of people.

## Challenges

AI in mental health also poses several challenges, including:
* Informed consent: AI-based systems are intricate, along with possessing biases and data-related complications. Properly informing patients of these drawbacks is crucial, though the responsibility falls in the hands of clinicians.
* Right to explanation: AI-based systems may initiate patient questions or desired expounding on diagnoses or suggested treatments which must be provided to patients upon their request.
* Patient privacy: AI-based systems must foster compatibility between the functionality of AI and the protection of those utilizing it to ease uneasiness towards the idea.
* Insufficiency of diversity: AI-based training must be wholistic to cater towards a diverse group of patients while providing comprehensive care, rather than disproportionately representing groups or being unskillful in supporting certain populations.
* Apprehension of providers and organizations: AI-based systems must be well grasped by those employed in healthcare and who serve complementarily to its functions, as a lack of accord between the two can diminish patient care.
* Tarasoff Duty: Since providers who are human have a real duty to warn people in the instance of where they perceive the patient is a risk to others or themselves, questions of who would bear that responsibility arise.
* Data acquisition and quality: Mental health data collection faces strict ethical and privacy constraints, and a great deal of related information is private and not publicly available. This makes it challenging for AI to access high-quality, diverse datasets. And lack of data or poor data quality can directly affect the accuracy and effectiveness of AI systems, thus preventing them from being widely used in real life.

## Current AI trends in mental health

As of 2020, the Food and Drug Administration (FDA) had not approved any artificial intelligence-based tools for use in Psychiatry. However, in 2022, the FDA granted authorization for the initial testing of an AI-driven mental health assessment tool known as the AI-Generated Clinical Outcome Assessment (AI-COA). This system employs multimodal behavioral signal processing and machine learning to track mental health symptoms and assess the severity of anxiety and depression. AI-COA was integrated into a pilot program to evaluate its clinical effectiveness, though it has not yet received full regulatory approval.
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.
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. 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. Research is also underway to develop a tool combining explainable AI and deep learning to prescribe personalized treatment plans for children with schizophrenia.
AI systems could predict and plan treatments accurately and effectively for all fields of medicine at levels similar to that of physicians and general clinical practices. For example, one AI model diagnosed depression and post-traumatic stress disorder with precision that exceeded that of general practitioners based solely on their assessments.
AI systems studying social media data are being developed to help spot mental health risks sooner and in more locations and for less money. Ethical concerns include uneven performance between digital services, the possibility that biases could affect decision-making, and trust, privacy, and doctor-patient relationship issues.
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.

## 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:
* Lack of data: There is a lack of data available to train AI systems, which limits their ability to accurately identify patterns in mental health conditions and predict outcomes.
* Bias: AI systems can be biased if the data used to train them is biased. This can lead to inaccurate predictions and unfair treatment of certain groups of people.
* Privacy: The use of AI in mental health raises concerns about privacy, as large amounts of personal data need to be collected and analyzed.
* Harmful Advice: The use of AI in mental health raises concerns about harmful advice being given since it is an AI ; one man killed himself after a chatbot told him to "sacrifice himself" and some chatbots have already been taken down due to the bad advice they gave
* Relationship: For decades research has consistently shown that the therapeutic relationship plays the most important role in whether and how therapy works.
* Empathy? Some question whether a human would experience empathy from an AI chatbot in the same way they would receive empathy from a human. As an AI has never experienced a heartbreak and does not know what addiction truly feels like, some question whether AI therapy can be considered a substitute for huma therapy

## Ethical issues

Although significant progress is still required, the integration of AI in mental health underscores the need for legal and regulatory frameworks to guide its development and implementation. Achieving a balance between human interaction and AI in healthcare is challenging, as there is a risk that increased automation may lead to a more mechanized approach, potentially diminishing the human touch that has traditionally characterized the field. 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. 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.