In a new study from Western University, researchers have developed an artificial intelligence (AI) tool that facilitates more personalized treatments for depression and improves patient outcomes.
They found that this new method of treatment selection improves the effectiveness of currently available treatments, with a small and affordable increase in overall treatment costs.
Current practice for treating depression often involves a stepped care approach.
Patients are first offered a low-intensity treatment, such as group therapy, with those who remain unwell later being moved to more intensive, lengthy treatment.
The researchers behind the new tool have shown that implementing AI helps patients receive more tailored care to treat their depression much quicker.
The use of the tool led to improvements in patient outcomes, increasing the probability of reliable and clinically significant improvements in depression symptoms.
The study marks the first clinical trial to demonstrate the benefits of using AI in mental health services.
The team says their machine-learning algorithm uses patients’ intake state of depression and anxiety, personality traits, level of social functioning, employment status and socio-demographic characteristics and predicts the type of treatment they would most benefit from.
Based on what the algorithm tells patients, some of them are stepped up to high-intensity treatment straight away rather than spending three to four months trying out treatment they are less likely to benefit from.
The tool was developed by analyzing data from more than 1,000 patients previously treated in psychological services to identify which treatments are most helpful for patients with specific symptoms and personality traits.
The clinical study itself involved more than 900 patients in Lancashire, Rotherham and Doncaster, U.K., through England’s National Health Service.
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The study is published in JAMA Psychiatry. One author of the study is Shehzad Ali.
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