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AI in Mental Health: Improving Diagnosis and Treatment of Mental Health Issues

Awareness around mental health has increased in recent years. As public figures openly talk about their struggles and younger generations use social media to talk about experiences with mental illness, interest in the matter has risen. In this context, among a wide variety of uses of artificial intelligence in medicine, healthcare professionals have found that AI-powered systems can play a big role in the future – and present – of mental health treatment.

Today,  we can finally acknowledge that mental health is an aspect of overall health. While in the past, the focus has been directed toward pathology, there was a shift in the way mental health is approached. Now it is mainly considered in terms of self-care. This new approach has opened a space for artificial intelligence to be part of this era of mental care.

How are AI and ML used in mental health?

The changes that AI is bringing – and will continue to bring – to mental health care encompass the whole journey of a mental health patient. The uses of AI in mental care can be distinguished between those oriented to diagnosis and those oriented to treatment.

AI and ML in mental health diagnosis

Whether we talk about text or images, the amount of data that intelligent machines can process is inaccessible to the human brain. AI has allowed physicians and researchers to take mental health diagnoses to a whole new level. 

We can mainly talk about three branches of Machine Learning technologies that can be used to diagnose mental health issues: 

  1. Computer Vision
  2. Machine Learning
  3. Natural Language Processing.

Let’s talk a little more about each of them.

1. Computer vision

Computer vision is a field of artificial intelligence that trains computers to understand and interpret the visual universe. Machine learning (ML) models can accurately perform tasks related to vision such as:

  • Identifying objects.
  • Classifying gestures.
  • Reacting to the images they “see”.

Patterns associated with mental health disorders can often be found in brain images. MRI or CT scans feed AI-powered systems that process images and trace these patterns to ease diagnosis.

2. Deep learning

Deep Learning (DL) is a type of machine learning that, inspired by the structure of the human brain, trains machines to learn by running data through neural network algorithms. Within the mental health diagnosis field, these algorithms have accomplished an enormous capacity for data analysis, which is used to predict the likelihood of a particular mental health diagnosis.

Some DL tools for enhancing predictive analysis are already making possible better outcomes for patients suffering from mental illness by:

  • Prescribing personalized treatment plans for children with schizophrenia.
  • Leveraging admission data of patients to predict the likeliness of a patient attempting to take their own life.

3. Natural language processing

Natural Language Processing (NLP) is a branch of Machine Learning that focuses on developing models that can analyze, interpret, and generate human language, mimicking humans’ ability to interact verbally.

Existing NLP models trained at speech recognition and text analysis can analyze discourse fragments to identify patterns associated with mental health issues.

NLP uses go even beyond diagnosis since NLP offers the most effective AI tools to improve mental health treatment.

ai in mental health

NLP and the new era of mental health treatment

Our mental well-being is mostly communicated through language. This is why psychologists, psychiatrists, and mental health specialists have historically relied on clinical dialogue and narrative as the main paths to assess mental health issues.

Today, AI development for mental healthcare provides professionals with new tools to continue exploring the power of language so that we can understand and tackle mental illness. However, ML tools for improving healthcare treatment do not exclude images. These are some of the most important uses of AI to improve mental health treatment:

What is an example of AI in mental health?

  • Virtual Therapists and Chatbots: provide mental health support, helping patients manage their symptoms and offering advice for self-care. 
  • Emotion Analysis: algorithms can analyze speech and body language to determine an emotional state and provide recommendations for personalized treatment.  
  • Virtual Reality Therapy: provides immersive experiences to help patients manage anxiety, phobia, PTSD, or other mental health challenges.
  • Mental Health Tracking: some apps are designed to help patients monitor their moods and symptoms continually.
  • Personalized Therapy: mental health apps can improve the effectiveness of treatments by identifying patients’ unique needs and providing tailored treatment options. 

Though these uses have all been proven effective for mental health treatment, AI still faces a lack of empathy – being that this last one is still exclusively human – that reduces the effectiveness of mental health treatment. 

Regardless, AI-powered assistants and chatbots evolved into more complex systems, and researchers have observed that people do tend to develop emotional connections with them. We could say AI can establish honest and open dialogues – even more, in some cases, than human beings. 

Specialists believe that anonymity, and the fact that people tend to consider robots non-judgmental and unbiased, contribute to this phenomenon. This type of openness is, of course, crucial to enhancing the ability of AI systems to offer practical advice and accurate diagnosis for patients coping with the effects of living with mental illness. 

ai and mental health

Why implementing AI in mental health care is crucial for the future (and what are the challenges for it)

The potential for bias is one of the most frequently mentioned challenges when it comes to implementing AI in mental healthcare. As it is human beings who choose the data used by algorithms and decide the way the results of those algorithms are applied, machine learning models can be influenced by unconscious human biases.

Another significant challenge is that, in many cases, the logic behind the decisions made by AI systems could be clearer, and this can lead to distrust. Since mental health diagnosis and treatment often involve sensitive patient data, organizations, companies, and institutions adopting AI for mental health will have to pay special attention to data privacy and security. Finally, developing training programs to help healthcare professionals keep up with AI technologies will be key for successful AI adoption. 

Despite mental health issues have gained more attention in recent years, it is clear that we are facing a global mental health crisis that is likely to continue. AI and ML-based systems and applications can give both mental health specialists and people experiencing mental health disorders or trying to improve their mental self-care the tools they need to make everyday lives better.

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