ABSTRACT
BACKGROUND: Mood disorders often are diagnosed by clinical interview, yet many cases are missed or misdiagnosed. Mood disorders increase the risk of suicide, making it imperative to diagnose and treat these disorders quickly. Artificial intelligence (AI) has been investigated for diagnosing mood disorders, but the merits of the literature have not been evaluated. This systematic review aims to understand and explain AI methods and evaluate their use in augmenting clinical diagnosis of mood disorders as well as identifying individuals at increased suicide risk. METHODS: We conducted a systematic literature review of all studies until August 1, 2020 examining the efficacy of different AI techniques for diagnosing mood disorders and identifying individuals at increased suicide risk because of a mood disorder. RESULTS: Our literature search generated 13 studies (10 of mood disorders and 3 describing suicide risk) where AI techniques were used. Machine learning and artificial neural networks were most commonly used; both showed merit in helping to diagnose mood disorders and assess suicide risk. CONCLUSIONS: The data shows that AI methods have merit in improving the diagnosis of mood disorders as well as identifying suicide risk. More research is needed for bipolar disorder because only 2 studies explored this condition, and it is often misdiagnosed. Although only a few AI techniques are discussed in detail in this review, there are many more that can be employed, and should be evaluated in future studies.
