Extracorporeal membrane oxygenation (ECMO) is a life-support technique used to treat cardiac and pulmonary failure, including severe cases of COVID-19 (coronavirus disease 2019) involving acute respiratory distress syndrome. Blood clot formation in the circuit is one of the most common complications in ECMO, having potentially harmful and even fatal consequences. It is therefore essential to regularly monitor for clots within the circuit and take appropriate measures to prevent or treat them. A review of the various methods used by hospital units for detecting blood clots is presented. The benefits and limitations of each method are discussed, specifically concerning detecting blood clots in the oxygenator, as it is concluded that this is the most critical and challenging ECMO component to assess. We investigate the feasibility of solutions proposed in the surrounding literature and explore two areas that hold promise for future research: the analysis of small-scale pressure fluctuations in the circuit, and real-time imaging of the oxygenator. It is concluded that the current methods of detecting blood clots cannot reliably predict clot volume, and their inability to predict clot location puts patients at risk of thromboembolism. It is posited that a more in-depth analysis of pressure readings using machine learning could better provide this information, and that purpose-built imaging could allow for accurate, real-time clotting analysis in ECMO components.