Artificial Intelligence (AI) is set to revolutionize the way disease is diagnosed and treated. This may be especially helpful for depression because it can lead to more accurate diagnosis and determine which treatments are more likely to work.
About 20% of us will have depression at least once in our lifetime. Worldwide, 300 million people are currently experiencing depression, with 1.5 million Australians likely to be depressed at any one time.
Because of this, depression has been cited by the World Health Organization as the single largest contributor to poor health worldwide.
So how can AI actually help?
Depression can be difficult to diagnose
Despite its frequency, depression is difficult to diagnose. In fact, it is so difficult that general practitioners are able to accurately diagnose depression in less than half of the cases.
This is because there is no single test for depression: doctors use self-reported symptoms, questionnaires and clinical observations to make a diagnosis. But the symptoms of depression are not the same in everyone.
Some people may sleep more, some may sleep less; Some people lack energy and interest in activities, while others may feel depressed or irritable.
For people who have been accurately diagnosed with depression, there are several treatment options available, including talk therapy, medications, and lifestyle changes. However, response to treatment is different for each person, and we have no way of knowing ahead of time which treatments will work and which will not.
AI trains computers to think like humans, with a particular focus on three human-like behaviors: learning, reasoning, and self-improvement (adapting and improving performance over time).
One branch of AI is machine learning, which aims to train computers to learn, find patterns in data, and make data-informed predictions without the guidance of humans.
In recent years there has been an increase in research applying AI to diseases such as depression, which can be difficult to diagnose and treat.
what have they found so far
Scientists have compared ChatGPT diagnoses and medical recommendations to real-life doctors, with surprising results. When presented with hypothetical patients of varying depression severity, gender, and socioeconomic status, ChatGPT mostly recommended talk therapy. On the contrary, doctors recommended antidepressants.
American, British and Australian guidelines recommend talk therapy as a first option of treatment before medication.
This suggests that ChatGPTs may be more likely to follow clinical guidelines, whereas GPs may have a tendency to overprescribe antidepressant medications.
ChATGPT is also less influenced by sex and socio-economic biases, while doctors are statistically more likely to prescribe antidepressants to men, especially those with blue-collar jobs.
How does depression affect the brain?
Depression affects specific parts of the brain. My research has shown that the areas of the brain affected by depression are very similar in different people. What’s more, we can predict with more than 80% accuracy whether someone has depression by looking at these brain structures on an MRI scan.
Other research using advanced AI models has supported this finding, suggesting that brain structure could be a helpful direction for AI-based diagnosis.
Studies using magnetic resonance imaging (MRI) data on brain function at rest can also correctly predict depression more than 80% of the time.
However, combining functional and structural information from MRI gives the best accuracy, correctly predicting depression in more than 93% of cases. This suggests that using multiple brain imaging techniques for AI to detect depression may be the most viable way forward.
MRI-based AI tools are currently used only for research purposes. But as MRI scans become cheaper, faster, and more portable, it’s likely that such technology will soon become part of your doctor’s toolkit, helping them improve diagnoses and enhance patient care.
Diagnostic tools you may already have
While MRI-based AI applications are promising, a simpler and easier way to detect depression may truly be available.
Wearable devices such as smart watches are being investigated for their ability to detect and predict depression. Smart watches are particularly helpful because they can collect a variety of data, including heart rate, step count, metabolic rate, sleep data, and social interactions.
A recent review of all studies conducted to date on the use of wearables to assess depression found that depression was correctly predicted in 70–89% of cases. Since they are typically used and worn around the clock, this research shows that wearable devices can provide unique data that may otherwise be difficult to collect.
However, there are some drawbacks, including the substantial cost of smart devices that may make them inaccessible to many people. Others include the questionable ability of smart devices to detect biological data in people of color and the lack of diversity in study populations.
Studies have also looked at social media to detect depression. Using AI, scientists have predicted the presence and severity of depression from the language of our posts and community memberships on social media platforms.
The specific words that were used predicted depression with up to a 90% success rate in both English and Arabic. The emojis we use have successfully detected depression in its early stages.
predicting responses to treatment
Several studies have shown that response to antidepressant treatment can be predicted with greater than 70% accuracy from electronic health records alone. This could provide doctors with more accurate evidence when prescribing drug-based treatments.
By combining data from people involved in trials of antidepressant drugs, scientists have predicted whether taking the drugs will help specific patients overcome depression.
AI shows substantial promise in the diagnosis and management of depression, although recent findings require validation before being trusted as a diagnostic tool. Until then, MRI scans, wearable devices and social media may be helpful in helping doctors diagnose and treat depression.
sarah hellewellResearch Fellow, Faculty of Health Sciences, Curtin University, and Perron Institute for Neurological and Translational Science, Curtin University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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