Artificial intelligence in mental health

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Background

In 2019, 1 in every 8 people, or 970 million people around the world were living with a mental disorder, with

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:

• 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.^[11]
• 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.^[11]
• 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.^[11]

Current AI trends in mental health

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:

• 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.^[16]
• 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.^[17]
• Privacy: The use of AI in mental health raises concerns about privacy, as large amounts of personal data need to be collected and analyzed.^[18]

See also

• Artificial intelligence in healthcare
• Artificial intelligence detection software
• AI alignment
• Artificial intelligence in healthcare
• Artificial intelligence
• Glossary of artificial intelligence
• Clinical decision support system
• Computer-aided diagnosis
• Health software

References