New machine learning tools are showing promise in detecting mental health disorders & distress where traditional screening programs fall short. In a summary paper that documented eight years of studies from the National Library of Medicine on PubMed.gov, investigators found that AI-powered tools are showing remarkable ability in the diagnosis of these and other conditions. If proven viable, it would mark a major advancement in the treatment of mental health disorders & distress. This comes at a critical time for the field as studies have shown over the years, that disorders and distress have grown over the years.
For many of these studies, they use video & audio files combined with the clinical history of individual patients to feed data into machine learning models. The goal of using all of this data is to train deep models for the automated detection of depression and/or other anxiety disorders. But though they show promise, there is a long way to go in training the data sets. Most of the issues are related to how distress and disorders can often manifest differently between cultures, genders, and ethnic groups.
These studies are similar to the ones that we reported on earlier in the fall. Researchers in Singapore’s NTU are hoping to discover biomarkers, combined with other patient data, to create a method of identifying mental illness through the use of artificial intelligence. One reason researchers are turning to AI-powered tools to help identify mental health disorders and directress is due to the shortcomings of traditional screening tools. Limitations of such tools include issues of the high prevalence of false positives, and the fact these tools were developed using those of the white ethnical group. This could be a problem because it poses a limitation in being able to identify at-risk markers that might not be seen in one ethnic group or another, so those in under-studied groups could be left undiagnosed.
All of this is a growing trend of artificial intelligence in medicine. The use of the technology is growing across the field with medical professionals and researchers employing the tools to bridge the gap between doctors and patients. It’s becoming increasingly useful and proven to use machine learning models to detect disorders and diseases. In some cases, far earlier than other methods are able to. Case in point, Parkinson’s Disease. MIT researchers used ML to create a modem-sized device that monitors patients as they slept, gathers data, and detects the disease via breathing patterns.
But, as of right now, many of these tools are still in the earliest of stages in development. Though they show promise in assisting those experiencing mental health disorders & distress, there is much that must be discovered about the role of genetics, the environment, and other factors.