Augmented Reality and Machine Learning in Health: A Systematic Review
Joseph Orji, Gerry Chan, Rita Orji · 2023
Augmented Reality (AR) is a useful technology for providing an information-rich reality by superimposing digital objects and giving a virtual interpretation of the physical environment. AR has played a key role in reducing cognitive load and the applications of AR have been useful in various fields ranging from manufacturing, advertisement, education, military, and health. AR has also been deployed on various platforms like mobile, computer screens, and head-mounted displays (HMD). In this paper, we systematically reviewed research papers that have applied AR systems with machine learning (ML) in various health-related domains within the past 12 years (2010–2021). We present a review of the state-of-the-art AR implementation and research in the area of health by (1) identifying various AR approaches, (2) uncovering various areas of health where AR have been applied, (3) determining the current trend, gaps, and areas for future work, (4) highlighting the artificial intelligence (AI) and machine learning (ML) algorithms used in the AR systems and how they are used, and (5) comparing the different visualization modalities (web, mobile, and HMD). This review adds to the existing literature by shedding light on the common tools, successful approaches used in implementing previous AR projects, and evaluation methods. We uncover how AI and object tracking was implemented in AR for health. Finally, we identify gaps and offer recommendations for advancing research in this area.