Abstract
It is widely acknowledged that over 90% of those who committed suicide had a psychiatric diagnosis at the time of death. Hence, suicide prevention is one of the top priorities for psychiatrists to address. However, the field of neuropsychiatry lacks biomarkers that could serve as objective indicators for diagnosis and severity assessment. Suicide is a complicated result caused by multiple factors and can be difficult to accurately predict. In recent years, research based on data analysis using artificial intelligence (AI) supported by machine learning has attracted attention in the medical field. In suicide prevention, an unprecedented amount of research using machine learning have been reported. In this paper, we will introduce new information about suicide prevention using machine learning as well as comment on the ethical, legal, and social implications (ELSI) that should be considered for implementation into society. There are no potential conflicts of interest to disclose.
