1 Faculty of Dentistry, McGill University, Montreal, QC, Canada; Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Jazan University, Jazan, Saudi Arabia.
2 Faculty of Dentistry, McGill University, Montreal, QC, Canada.
3 Concordia University, Library, Montreal, QC, Canada.
4 Faculty of Dentistry, McGill University, Montreal, QC, Canada; Royal Medical Services, King Hussein Medical Center, Jordan.
5 Faculty of Dentistry, McGill University, Montreal, QC, Canada; Faculty of Medicine, Laval University, Quebec City, QC, Canada.
6 Faculty of Dentistry, McGill University, Montreal, QC, Canada; Université Claude Bernard Lyon 1, Faculté d'Odontologie, Lyon, France.
7 Faculty of Dentistry, McGill University, Montreal, QC, Canada; College of Dental Medicine, Qatar University, Doha, Qatar. Electronic address: faleh.tamimimarino@mcgill.ca.

Clinical performance of osseointegrated implants could be compromised by the medications taken by patients. The effect of a specific medication on osseointegration can be easily investigated using traditional systematic reviews. However, assessment of all known medications requires the use of evidence mapping methods. These methods allow assessment of complex questions, but they are very resource intensive when done manually. The objective of this study was to develop a machine learning algorithm to automatically map the literature assessing the effect of medications on osseointegration. Datasets of articles classified manually were used to train a machine-learning algorithm based on Support Vector Machines. The algorithm was then validated and used to screen 599,604 articles identified with an extremely sensitive search strategy. The algorithm included 281 relevant articles that described the effect of 31 different drugs on osseointegration. This approach achieved an accuracy of 95%, and compared to manual screening, it reduced the workload by 93%. The systematic mapping revealed that the treatment outcomes of osseointegrated medical devices could be influenced by drugs affecting homeostasis, inflammation, cell proliferation and bone remodeling. The effect of all known medications on the performance of osseointegrated medical devices can be assessed using evidence mappings executed with highly accurate machine learning algorithms.