AI-Enhanced Diagnostic Imaging via Internet of Everything

Abstract

This study aims to investigate how artificial intelligence algorithms can be used to remove connected bias from enormous quantities of medical imaging data, hence improving the accuracy and precision of diagnosis. It is possible to facilitate more accurate and rapid judgments by utilizing IoE, which allows for the seamless participation and reuse of data from various medical detectors and imaging instruments in real time. In addition, the study considers the possibility that artificial intelligence would one day be able to recognize patterns and anomalies that human clinicians might overlook, which might result in the earlier detection of conditions and improved case management. As an additional benefit, the incorporation of IoE improves the accessibility and availability of individual services, particularly in locations that are underserved or considered remote. In this examination, the obstacles and potential directions in this sector are brought to light. These challenges include the necessity for adequate AI training datasets, patient sequestration, and data security.

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