Managing Depressive Disorders with Artificial Intelligence
Written by Jaishree Ramamoorthi
Edited by Cindy Ho
January 5, 2023
Edited by Cindy Ho
January 5, 2023
As one of the most common psychiatric illnesses in the world, research on the management of depressive disorders has increased rapidly over the years. A significant amount of this research is through the application of artificial intelligence (AI). Using AI in medicine has been of notable interest since the surge of technological advancements in the field. With the potential to diagnose and determine therapeutic plans rapidly, using AI in medicine can decrease the overall processing times that patients face.
Since AI replicates human cognitive functions, the application of AI in neuropsychiatry is highly promising. With machine learning, AI can mimic the mechanisms humans use to retain information, reason, and make decisions. Essentially, this allows technology to analyze data based on what they have “learned” from previous data sets.
The application of machine learning has promising effects in managing depression. For instance, combining biological, clinical, and psychological datasets together has enabled clinicians and researchers to predict the likely course of a patient’s depression. In conjunction with neuroimaging techniques, machine learning can classify major depressive disorders and inform treatment decisions. As a result, machine learning in AI can streamline the process of diagnosis and treatment.
While research in the medical application of AI in neuropsychiatry has escalated, there are still many challenges to overcome before it can be used to manage depressive disorders. For one, there is not enough research on the roles of privacy and confidentiality of patient data. Although the patients’ information is anonymous and protected, the advancement of technology increases the susceptibility to potential data breaches. Especially since mental illnesses are still highly stigmatized, insurance companies can use AI to deny patients of their proper healthcare benefits. Additionally, the integration of diverse datasets into algorithms that are effective in diagnosing and determining treatment plans is very difficult to achieve. Consolidating data related to demographics, social networks, genetics, pathology, and others will require additional research and tests. Aside from that, research comparing the therapeutic plans formed by AI and the therapeutic plans formed by psychiatrists is still very limited. More research is needed to determine AI’s efficacy in forming treatment plans and where adjustments need to be made.
Ultimately, the implementation of AI in the management of depressive disorders has promising effects because it can quickly and effectively diagnose and treat patients. However, there is still much work to be done before it can be used in a clinical setting. Most importantly, since depressive disorders affect such a large population, creating a systematized procedure to diagnose and treat patients can save time in hospitals and benefit a lot more people.