Artificial intelligence (AI) has shown promise in helping diagnose and monitor mental health issues. However, it has drawbacks. It can be prone to data overfitting, which may result in false positives. In addition, physicians must be trained in using AI effectively to ensure its safe and effective implementation. Fortunately, new research into AI in college student mental health is addressing these concerns and paving the way for a more nuanced understanding of this technology’s prospective applications.
Current treatments for psychiatric disorders mostly rely on medication and clinical psychotherapy. However, these interventions can be expensive and sometimes have undesirable side effects. AI methods can help alleviate these problems by predicting symptoms and creating tailored treatment plans. This is a critical area of research because it can reduce the burden on physicians already struggling with limited time and documentation needs.
Traditionally, mental illnesses are identified based on self-reports of symptoms and a clinician’s diagnosis using a manual such as the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), which is heavily focused on specific symptoms. However, these assessments are often subjective and time-consuming. AI can overcome this limitation by parsing vast amounts of data from various sources, including electronic health records, social media, smartphone and video monitoring systems, and brain imaging.
AI can identify individuals who are more likely to develop a mental illness by analyzing the structure of their brains. For example, researchers at the University of California, Davis, used machine learning to identify individuals with a schizophrenia risk factor based on computer vision analysis of brain images. Other studies have found that AI can predict suicidal behavior based on a combination of demographic information and individual differences such as personality traits, genetics, family history, environment, and lifestyle.
One study analyzed a large sample of medical records, behavioral data, and voice recordings from people who called crisis hotlines to determine whether AI could identify warning signs of suicide with 80% accuracy. Another study at Vanderbilt University Medical Center found that a computer could identify the most likely causes of someone’s suicide by evaluating the content of their last email or text message.
While AI can be a powerful tool in the fight against mental illness, researchers and healthcare professionals must continue working together to ensure its proper implementation and use. This requires a thorough and rigorous review process to prevent overfitting, which can lead to inaccurate predictions and can create a bias toward certain groups of people. It also requires that researchers focus on “explainable” AI so that algorithms can be understood by doctors who are not experts in machine learning. This will allow them to identify and mitigate potential ethical risks from applying this technology in their practices.
