Artificial Intelligence, Machine Learning, and Internet of Drones in Medical Applications

Background

In this sample, various healthcare participants were extensively studied, including those involved in biomedical supply transport, emergency first responders, and telemedicine providers. While these are natural categories for academics seeking to have their drone applications implemented by healthcare policy and decision makers, little thought was given to how such applications might affect patient groups and communities. This begs the question of who is developing drone health applications and why. Eight of the studies had no authors with a background in health, medicine, or health-related fields. Working with target communities was advocated by less than one-third of the author groups (Claesson et al., 2017). Even fewer people actually follow through on their invitations to participate (Mulero et al., 2017). Taken together, these characteristics of drones for health indicate that some may be more interested in leveraging the health context to advance drone technology and markets than in designing drones to meet a specific health need. While this may not be a problem in some cases, it can be a problem when people with a good understanding of patient and healthcare system needs, such as system users, front-line workers, and administrators, are not given the opportunity to develop competence and autonomy in their own field. Engagement with a broader range of users may encourage the development of health-related drone apps that take a variety of health and digital literacies into account, facilitating their long-term integration into healthcare (Glauser et al., 2018).