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In order to interact socially appropriately, humans rely on our capacity to accurately infer the intentions and emotions of other people. Conversely, a human-machine collaboration environment is born out of AIEd, or the use of AI in education. This, in turn, modifies the manner in which individuals interact with one another and may have an effect on them. This research was conducted with the intention of determining whether or not being exposed to AIEd had an impact on the emotional perception of adolescents. It is becoming increasingly plausible that artificial intelligence (AI) will be included into clinical treatment on a daily basis in the not too distant future. This is due to the growing body of evidence demonstrating AI's ability to enhance several facets of healthcare delivery. As a result of this potential, governmental bodies and technical firms are placing a greater emphasis on and expanding their investments in artificial intelligence medical applications. Concern, on the other hand, has been voiced over the ethical and regulatory implications of implementing AI in the medical field. Furthermore, AI could be biased, some algorithms aren't very open about their inner workings, data used to train AI models could be private, and there are safety and liability problems with using AI in healthcare settings. All of these difficulties are related to artificial intelligence. While much has been said about the ethical considerations of AI in healthcare, very little has been said on how to really address these issues in the industry. This article's goals are twofold: first, to foster additional conversation about how to regulate AI in healthcare by outlining a governance model that attempts to handle the ethical and regulatory challenges that emerge from implementing AI in healthcare.
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