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AI and Psychiatry

WHAT EXACTLY IS PSYCHIATRY? 

 

The human psyche and the art of understanding it requires deep complexity as this field has a lot to offer. The psyche isn’t just the mind, and it isn’t just the workings of the brain, not only they are just emotions but there’s a lot more to it. 

Psychiatric disorders are one of the leading causes of disability worldwide, affecting individuals from an early age thus, leading to disability and even death at times. Over the past few years, the understanding of psychiatry has flourished with the aid of Artificial Intelligence and technology opening doors with its ultramodern visualization and elevation. 

 

 

AI AND PSYCHIATRY: 

AI in psychiatry implies the use of advanced computerized techniques such as automated language processing and machine learning for assessing a patient’s mental state beyond what could be measured with self-reports and clinical observations. AI could provide a promising opportunity for enhancing and even revolutionizing interventions with psychiatric disorders. 

  • AI in Diagnosis:

The amount of progress in the past years in understanding psychiatric disorders has proven to be astounding, however, the ability of the clinicians to diagnose the disorders have been obstructed by the lack of objective and clinical measures. Now that’s where AI comes in, as it is a promising approach in the diagnosis of psychiatric disorders. More recently, machine learning techniques have been used to improve the discrimination between healthy individuals and adults with ADHD.

Moreover, People tend to resist change, and while newer is not always better, being open to thoughtfully novel approaches moves healthcare forward. Machine-learning is a powerful tool for looking at massive data sets and discovering useful patterns in data that other techniques might not be able to. By using Ai-type approaches, researchers can leverage computation power to see consistencies in how symptoms cut across received diagnostic categories to develop ‘transdiagnostic’ perspectives. 

  • AI in Prediction:

AI-based techniques have also been effectively used in the prediction of psychiatric symptoms. AI would present its findings as a number—like a blood-pressure reading—that a psychiatrist could take into account when making a diagnosis.  And as the algorithm is “trained” on more and more patients, that reading could better reflect a patient’s state of mind. Rather than trying to help diagnose a mental disorder from a single sample, the AI would examine a patient’s speech over time to track their progress. AI machines, if trained properly, can use computational algorithms and statistical models that can automatically infer hidden patterns from data. In recent years, there has been an increase in the number of Machine Learning models being developed to analyze health care data. However, approaches of these types require a significant amount of feature engineering to obtain optimal and accurate results, which usually proves to be time-consuming. 

The influence on NEUROIMAGES 

Neuroimages can be used to record evidence of neuropsychiatric disorders. Two common types of neuroimage data that are used for analysis in mental health studies are

  • Functional magnetic resonance imaging (fMRI) 
  •  Structural MRI (sMRI) data. 

Deep Learning proves to be effective in fMRI and MRI data to identify symptoms of ADHD. To get meaningful information from the neuroimages, Deep Belief Network (DBN) models are used to obtain a deep hierarchical representation of the neuroimages. In addition to that, Convolutional Neural Network (CNN) models are used to identify local spatial patterns. 

  • AI-based Therapy:

The growing demand for healthcare and increasing Psychiatric disorders coupled with limited resources has created opportunities for digital and technical solutions such as AI to help solve the challenges in the field of psychiatry. Ai can be used for improvements in clinical outcomes and patient safety, cost reductions, population measurements, and advancements in research. Computer-assisted therapy (CAT) offers exciting prospects by delivering some aspects of psychotherapy or behavioral treatment. CAT typically consists of programs made up of videos and questionnaires that are delivered to the patient through a computerized platform to help him cope w

ith his symptoms. Moreover the fact considering that the Internet is intricately merged into our daily lives, e-therapies could be an effective way to provide support for individuals suffering from mental health disorders. 


  • Treatment:

As we have seen so far, mental health is an area of health care that can be delivered via telehealth without losing its essence. The advantages of equipping AI in this area would be numerous if done properly since the factor of human error would be eliminated. As mentioned before,

 certain algorithms have already been proven to be successful at detecting signs of depression, PTSD, etc by analyzing speech patterns and facial expressions. Other than that, the meetings between a patient and his therapist are usually brief since the therapist is almost always seeing other patients as well and because of this, he might not notice wh

en patients exhibit subtle signs of trouble.

To make things even easier, rather than seeing a therapist, a person can answer some questions on an app that would detect early signs of trouble, if any. Another benefit of introducing AI in psychiatry is the 24/7 availability of support. The accessibility of chatbots proves extremely helpful to people who are not comfortable in sharing their feelings with a person. It should be kept in mind that AI can’t completely remove therapists and its introduction in the field of psychiatry is still new. That being said, it does have the potential to revolutionize mental healthcare, ultimately making care more accessible, more responsive, and more affordable.

 

THE CURRENT STATE OF AI IN PSYCHIATRY:

In the present state, there aren’t many clinical frontline applications, that have become national standards or things that people always use. 

The AI models that are currently in operation can predict when someone may be safe to leave the hospital or when there’s a lower risk of suicide, what medications someone could respond to, and the need for psychiatric beds. But when it comes to aiding diagnosis or managing patient health care AI tools may be trickier and less beneficial psychiatry is not something like radiology or pathology, where one can do a pattern-based diagnosis. NO doubt that machine learning algorithms have been used to distinguish brain differences between people with and without particular disorders such as Alzheimer’s disease, schizophrenia, and depression.  However, these findings haven’t been able to make the leap to clinical care. Now,  our brain functions change when performing different tasks, and this variation is something that traditional technology cannot really capture. Neuroimaging can create a snapshot of a person’s brain activity at a particular moment, but that moment is bound to pass, and then what? When it comes to psychiatry we need things that can be scalable, of course, we are talking about the lives of millions of people and there’ll probably be more with this pandemic, unfortunately. So we cannot just diagnose a patient based on speculations and risk their lives. 

Pros and cons of AI in psychiatry:

PROS:

Even though mental health victims all around the globe are increasing alarmingly, the topic remains taboo with a majority of the victims facing a lack of awareness or an unwillingness to open up. Although AI and psychiatry seem like opposite ends of a spectrum,  that is not true anymore. Patients who cannot afford professional help can turn to virtual counselors (chatbots) and attain satisfactory treatments. Examples of such mental health chatbots include Woebot and Wysa that utilize AI, CBT, and NLP to script responses while dealing with their patients. Other than that, they also host meditation exercises on better calming and sleep techniques. Most of these features are free of cost, making them accessible for patients without income. The fact that Wysa has over 1 million downloads on the Google Play Store shows the trust people have put in this technology. One of the prime reasons why many adolescents and young teens go for virtual help is the anonymity it offers. Another important reason is that AI can detect patterns in speech that can be difficult for humans to detect. Furthermore, these chatbot applications are available to engage in a conversation anytime and, therefore, the patient doesn’t have to worry about any appointments.

 

CONS:

Virtual chatbots, no matter how well trained, cannot replace the services provided by licensed therapists. A bot might reply with something that can trigger the anxiety of the patient, worsening their mental health. For example, according to the cautionary message of Wysa, the bot “is not designed to assist with crises such as abuse, severe mental health conditions that may cause feelings of suicide, harm to self, and any other medical emergencies. It can only suggest that users seek advanced and professional medical help.” 

In simple words, AI-based technology can only be used as a supplement for existing psychiatric health care resources.

THE FUTURE OF PSYCHIATRY:

With the mental problems arousing every day, it was predicted in a report in 2017 that psychiatrists’ demand for their services outpaces supply. But some proponents said that, in the future, an unlikely tool—artificial intelligence—may be ready to help mental health practitioners mitigate the impact of the deficit. And they weren’t wrong about that. 

 If you are one of the 3.5 billion people who own a smartphone, then it is safe to say that the future of psychiatry might be in your pocket. With data from smartphone sensors, psychiatrists could learn more about the patient than seeing them personally. AI has the potential to create a whole new science of human behavior. It holds the answer to various complex questions in psychiatry. Medicine is a fruitful area for artificial intelligence; it has shown promise in diagnosing disease, interpreting images, and zeroing in on treatment plans. Though psychiatry is in many ways a uniquely human field, requiring emotional intelligence and perception that computers can’t simulate. But in the near future, AI could have a huge impact. The field of psychiatry could benefit from artificial intelligence’s ability to analyze data and pick up on patterns and warning signs so subtle humans might never notice them. 

Conclusion:

Research and findings collected over the past few years have proven the fact that AI-based interventions have revolutionized the process of diagnosis, prediction, and treatment of psychiatric disorders. Computerized techniques such as automated speech language analysis could provide the groundwork for future developments of reliable clinical tests for psychiatry, and may change the way clinicians treat psychiatric disorders. Innovations such as CAT and MOST have already proved effective in ameliorating symptoms of depression, anxiety, and/or psychosis through online peer support, and could present an opportunity to facilitate the delivery of tailored therapy content while facilitating privacy and autonomy.

Ultimately, AI might one day be used for more than helping people manage their mental disorder symptoms—it could help prevent mental illness and even help people flourish. AI has already outsmarted the minds of neurological surgeons and in the future, I believe that AI will be leading the world of psychology as well. 

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Written by: ALI NADEEM AND SYEDA KHADIJA ABIDI

Edited By: SYED SANNAN ALI

 

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