Advances in mental health diagnosis through artificial intelligence in discourse and voice analysis: a systematic literature review

Abstract

The application of automated discourse and voice to assess mental health has been a growing field, as it can enhance diagnostic procedures. This study explores the application of automated systems for discourse and voice analysis to assess an individual’s mental health. It portrays AI's potential to improve diagnostic accuracy, allowing early intervention through risk factor identification This systematic review utilized the PRISMA Methodology. Ten articles were analyzed regarding ML, DL, and NLP-based programs to identify discourse and vocal alterations related to mental health. The implications of this study suggest that AI can lead to more accessible, cost-effective, and personalized mental health resources, ultimately improving patient outcomes and allowing clinicians to focus on human connection services. However, further research is required to standardize AI techniques and programs to validate their effectiveness in diverse clinical settings.