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Why and How AI technologies and Medical Data Analytics can support the development of Public Health Policies
The road to public health policies passes inevitably from its core element, the human, and the promotion of individual health and well-being. The improvements in targeted against ‘one size fits all’ clinical decisions, treatment plans, and predictions of risk stratification have been pursued through the development of AI technologies that integrate seamlessly heterogeneous health data like medical images, omics, lab tests, socio-demographic particularities to better identify and treat an individual’s disease.
Smart wearables, mobiles, and biosensors is a consistent ally to this effort since personal health-related data produced by such devices can further enhance the electronic health record and provide the source of meaningful patterns concerning the real-time health status, adherence to medical advice, the existence of health emergencies and the delivery of personalized consultation that is tailored made on the person’s needs. Towards this purpose, the European Commissioner for the Digital Agenda and Commission Vice-President shared her vision for the personal health navigator, which is a mobile application that acts as a coach for the engagement and support of healthy living of the individuals, while enabling data sharing and communication in a cross-border context.
Due to the advances in edge computing, the extraction of knowledge from these patterns can be conducted in-site (where the patient is located) by smart AI agents to reduce undesired latencies or n a centralized server for analysis on a larger scale. Being able to capture the variety of evidence for an individual’s health status will lead to the creation of a computation avatar that can be exploited as a simulation test bench for the prediction of pathophysiology progression and the effects of received therapeutics. All the aforementioned technologies can form a collaborative ecosystem that promotes the idea of personalized medicine that, in the eye of an inattentive reader, might seem the very opposite to public health.
However, this hypothesis is far from reality. From one point of view, focusing on the individual traits hidden in the connections of various omics can unveil new knowledge for a more precise treatment plan against a disease of a more effective prevention program. Moreover, the application of personalized medicine requires intensified data sourcing in the sense that health professionals and, even patients to a certain extent, bear increased responsibilities as they are dynamically involved in the data gathering.
The accountability in the process of data gathering contributes to public health policies with more data of better quality. The opposite contribution is observed in the process of aggregating extracted knowledge from personalized medicine into larger health-related information groups by gaining insights from population-level data. This reciprocal communication highlights that personalized medicine and public health cannot be regarded as contradicting notions.