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Blockchain technology and federated machine learning for collaborative initiatives in orthodontics and craniofacial health
Authors:Veerasathpurush Allareddy  Sankeerth Rampa  Shankar Rengasamy Venugopalan  Mohammed H. Elnagar  Min Kyeong Lee  Maysaa Oubaidin  Sumit Yadav
Affiliation:1. Department of Orthodontics, University of Illinois Chicago College of Dentistry, Chicago, Illinois, USA;2. Rhode Island College, Providence, Rhode Island, USA;3. Tufts University School of Dental Medicine, Boston, Massachusetts, USA;4. University of Illinois Chicago College of Dentistry, Chicago, Illinois, USA;5. UNMC College of Dentistry, Lincoln, Nebraska, USA
Abstract:There is a paucity of largescale collaborative initiatives in orthodontics and craniofacial health. Such nationally representative projects would yield findings that are generalizable. The lack of large-scale collaborative initiatives in the field of orthodontics creates a deficiency in study outcomes that can be applied to the population at large. The objective of this study is to provide a narrative review of potential applications of blockchain technology and federated machine learning to improve collaborative care. We conducted a narrative review of articles published from 2018 to 2023 to provide a high level overview of blockchain technology, federated machine learning, remote monitoring, and genomics and how they can be leveraged together to establish a patient centered model of care. To strengthen the empirical framework for clinical decision making in healthcare, we suggest use of blockchain technology and integrating it with federated machine learning. There are several challenges to adoption of these technologies in the current healthcare ecosystem. Nevertheless, this may be an ideal time to explore how best we can integrate these technologies to deliver high quality personalized care. This article provides an overview of blockchain technology and federated machine learning and how they can be leveraged to initiate collaborative projects that will have the patient at the center of care.
Keywords:artificial intelligence  blockchain technology  collaborative care  federated machine learning  Team sciences
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