BACKGROUND
Artificial Intelligence (AI)-driven serious games have been used in healthcare to offer a customizable and immersive experience. Summarizing the features of the current AI-driven serious games is very important to explore how they have been developed and used and their current state in order to plan on how to leverage them in the current and future healthcare needs.
OBJECTIVE
The current study aimed to explore the features of AI-driven serious games in healthcare as reported by previous research.
METHODS
We carried out a scoping review to achieve the above-mentioned objective. The most popular databases in information technology and health fields (e.g., MEDLINE and IEEE Xplore) were searched using keywords related to serious games and AI. These terms were selected based on the target intervention (i.e., AI) and the target disease (i.e., COVID-19). Two reviewers independently performed the study selection process. Three reviewers independently used Microsoft Excel to extract data from the included studies. A narrative approach was used for data synthesis.
RESULTS
The search process returned 1470 records. Of these records, 46 met all eligibility criteria. 60 different serious games were found in the included studies. Motor impairment was the most common health condition targeted by these serious games. Serious games in most of the studies were used for rehabilitation. The serious games in the majority of the included studies can be played by only single player. Most serious games were played on standalone devices (offline games). The most common genres of serious games were role-playing games, puzzle games, and platformer games. Unity was the most prominent game engine used to develop serious games. Personal computers (PCs) were the most common platforms used to play serious games. The most common algorithms used in the included studies were Support Vector Machine (SVM), Convolutional Neural Network (CNN), Artificial Neural Networks (ANN), and Random Forest (RF). The most common purposes of AI were the detection of disease and the evaluation of user's performance. The dataset size ranged from 36 to 795,600, with an average of about 52,124. The most common validation techniques used in the included studies were K-fold cross-validation and training test split validation. Accuracy was the most commonly used metric to evaluate the performance of AI models.
CONCLUSIONS
The last decade witnessed an increase in the development of AI-driven serious games for healthcare purposes and targeting various health conditions and leveraging multiple AI algorithms; this rising trend is expected to continue for years to come. While the evidence uncovered in this study shows promising applications of AI-driven serious games, larger and more rigorous, diverse, and robust studies may be needed to examine the efficacy and effectiveness of AI-driven serious games in different populations with different health conditions.