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Enhancing pain management through self-pain-perception holds great promise. In this systematic literature review, 26 different approaches to pain recognition are examined, providing a comprehensive evaluation framework based on six key criteria: the robustness of the model, the accuracy achieved, the reproducibility, intrusiveness, cost, and general usability in each scenario. Using our rating framework, we evaluate the identified pain recognition systems on their own merits with the examination of model complexity entailing an assessment of the technical complexity involved in implementing a particular pain recognition model, referring to the level of sophistication and complexity of the computational framework, algorithms, and methodologies employed within the model or are required for the execution of the particular method. The accuracy of the system in detecting pain controls the precision grade obtained, while repeatability measures the stability of a system by giving consistent results across multiple trials and fold validations. Intrusiveness examines the extent to which the system interferes with the natural pain experience. Cost analysis helps researchers and institutions weigh the financial implications associated with each option. Finally, usability captures the versatility and adaptability of the system to different pain perception situations. Our primary objective is to provide valuable assistance to researchers and healthcare facilities that find themselves in a position to decide which pain recognition methods to implement, often uncertain about which approach would be more suitable for addressing a specific problem. There are many approaches taken in producing such pain assessment models while the two most prominent are EEG signal evaluation and image based systems that do not come in contact with the patients body. Both approaches can use either Deep Learning methods to evaluate the signals, or more traditional algorithms such as Support Vector Machines. We have found that the best-performing system is a combination of the two models: one based on Deep Learning and another based on Support Vector Machines.Keywords: pain-detection, method assessment, six-criteria, deep learning

Copyright © 2025 konstantinos fragkos. This is an open access article distributed under the Creative Commons Attribution License.