Nearly 60 million people in the United States struggle with mental illness each year. Yet, healthcare providers identify depression in only 47% and detect clinical deterioration in only 21% of cases. Detection failures contribute to dire consequences in the United States, where suicide is the ninth-leading cause of death in adults and the second-leading cause of death in young adults. Initiatives to support busy clinical teams to screen via patient surveys or clinician interviews reliably have fallen short. Advances in technology enable automated assessment tools that complement existing workflows. In this project, we aim to implement novel artificial intelligence algorithms to aid in detecting signs of clinically significant depression and anxiety.