Detecting viruses, which have significant impact on health and the economy, is essential for controlling and combating viral infections. In recent years there has been a focus towards simpler and faster detection methods, specifically through the use of electronic-based detection at the point-of-care. Point-of-care sensors play a particularly important role in the detection of viruses. Tests can be performed in the field or in resource limited regions in a simple manner and short time frame, allowing for rapid treatment. Electronic based detection allows for speed and quantitative detection not otherwise possible at the point-of-care. Such approaches are largely based upon voltammetry, electrochemical impedance spectroscopy, field effect transistors, and similar electrical techniques. Here, we systematically review electronic and electrochemical point-of-care sensors for the detection of human viral pathogens. Using the reported limits of detection and assay times we compare approaches both by detection method and by the target analyte of interest. Compared to recent scoping and narrative reviews, this systematic review which follows established best practice for evidence synthesis adds substantial new evidence on 1) performance and 2) limitations, needed for sensor uptake in the clinical arena. 104 relevant studies were identified by conducting a search of current literature using 7 databases, only including original research articles detecting human viruses and reporting a limit of detection. Detection units were converted to nanomolars where possible in order to compare performance across devices. This approach allows us to identify field effect transistors as having the fastest median response time, and as being the most sensitive, some achieving single-molecule detection. In general, we found that antigens are the quickest targets to detect. We also observe however, that reports are highly variable in their chosen metrics of interest. We suggest that this lack of systematisation across studies may be a major bottleneck in sensor development and translation. Where appropriate, we use the findings of the systematic review to give recommendations for best reporting practice.