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Can AI Help Doctors Treat Depression? These Startups Think So

While artificial intelligence and machine learning are increasingly powering a digital health boom, AI is still very much in its infancy when it comes to mental and behavioral well-being. This isn’t really a surprise, as the ability to understanding human thoughts and feelings --not ‘merely’ crunching blood test data or medical scans for signs of disease-- is much harder than telling if a person’s kidney is about to fail. Boosting one’s mood or providing personalized treatments for psychiatric disorders, and particularly depression, is harder still.

Depression is the world’s leading health burden according to the World Health Organization. Patients often face tedious trial and error processes to navigate the ocean of antidepressants. Many patients are dissatisfied with primary healthcare services, do not adhere to treatment, and report debilitating side effects. Recent research shows that two thirds of patients fail to get better following taking their first-time antidepressant and a further 30% completely quit their first line of treatment.

To tackle depression and anxiety, startups are coming out with a range of AI-based tools designed to help psychiatrists and family doctors  optimize care sooner. Taliaz, a Tel-Aviv based startup, is developing an AI-driven decision support tool called PREDICTIX that helps doctors identify the right antidepressant medication. PREDICTIX was developed based on data from the largest prospective clinical trial on depression to date, the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) Study.  Taliaz says that after developing the algorithms, the tool was able to predict efficacy and adverse effects of current antidepressants, with up to nearly 75% accuracy.


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