Sensor-Based Virtual Reality for Clinical Decision Support in the Assessment of Mental Disorders

Recent reports show that 1 in 4 families has at least one member with a mental disorder. In the current practice, most diagnosis methods in psychiatry are based on clinical interviews and questionnaires, which are subjective and can lead to recalls and interviewer biases. In the healthcare context, Virtual Reality (VR) has shown a strong potential to improve decision making and help patients to better connect with reality, cope with pain, and overcome mental disorders such as anxiety and depression. This study integrates sensing technology (i.e., eye tracking) with a VR simulation of healthcare environments to improve the clinical decision-support system for diagnosis and assessment of mental disorders. Traditional scenario-based patient simulations are used as a basis for the development of VR modules. Data collected via the eye-tracking sensing technology are utilized to develop analytical models for predicting the risk of mental illness. Moreover, artificial intelligence (AI) tools for VR-based healthcare training help medical students learn faster and make smarter decisions. This research helps contribute to improved population health by developing new methods for promoting health and effectively predicting and treating mental disorders.