Application of Immersive Technologies and Machine Learning in Diagnosis and Treatment: A Systematic Review

The application of Augmented Reality (AR) and Virtual Reality (VR) integrated with Machine Learning (ML) in healthcare for disease treatment and diagnosis has significantly increased over the years. The purpose of this systematic review is to synthesize the integration of ML with AR/VR in the health domain, with a focus on various diseases. The review offers a comprehensive analysis and insights into trends in publication venues, application domains, development phases, and ML applications, highlighting the use of VR and AR technologies across various medical and psychological fields. Additionally, it investigated the features of the AR/VR applications in the reviewed studies and the targeted users discussed in the publications reviewed. A total of 43 publications were included in this review, covering research from the past 10 years (2014–2024). Our results show that (1) the use of AR/VR-ML has generally increased over the past decade, (2) VR is the most common type of application, (3) most studies used multiple ML algorithms together, (4) adults are the primary target demographic, children ranked second, with the elderly ranked least, and (5) Head-Mounted Displays (HMDs) are the most common platform. Overall, AR/VR/ML systems show significant potential for supporting capabilities in diagnosing and treating many types of medical conditions; however, a large variability exists among the different studies due to differences in the design and evaluation of the studies. Further research is needed to inform the real-world adoption of AR/VR-ML systems through longer-term clinical studies, transparent data reporting, and evaluation of practical considerations, including cost, accessibility, and workflow integration.