## 4 Discussion  
The literature search yielded 68 studies concerning the application of biomedical thermal data with ML strategies. More than half of these were focused on breast cancer diagnosis, yet its usage as a primary screening method is not recommended [80]. Figure 4: Forest plot for log diagnostic odd ratios: all studies combined. Figure 5: Summary receiver operating characteristic (SROC) curve for diagnostic test accuracy and the respective 95% confidence interval region (95% CI region). There is a clear preference for the use of ANN (19 studies), SVM (17 studies) and k-NN (9 studies) classifiers to achieve the best possible classification metrics, with only some authors choosing ensemble approaches (9) (Appendix A). The large number of successful implementation strategies already published, and the availability of pre-written algorithms based on these learners in development and computing environment tools such as WEKA, Spyder and Matlab, could be a justification for this choice. However, in some cases, this leads to a poor or inexistent description of classification parameters, training/testing conditions and/or number/type of input features selected, hindering the reproducibility of such studies. The inexistence of staple classification parameters is also clear and hampers a thorough meta-analysis. When looking for breast cancer results, maximum performance was achieved by [39], while [53] showed the best approach to aid diabetic foot diagnosis. Still, additional work can be done to better the current methodologies. For future work, most authors emphasize the need for improved feature selection strategies to guarantee the inclusion of significant features, while keeping the number of classification inputs as low as possible, thus reducing processing time [9; 15; 17; 18; 29; 37; 39; 41; 53; 63; 65]. To improve classification metrics, IR data could be complemented with information collected from other imaging modalities and/or biological tests [9; 26; 33; 64; 70]. The availability of a larger data sample is also mentioned by several studies across the different pathologies, in order to perform more complete testing and ease the implementation of such methodologies in daily practices [9; 16; 25; 26; 31; 44; 46; 52; 53; 54; 58; 60; 63; 65; 67]. Apart from the mentioned suggestions, the implementation of parameter optimization during the construction of the learner may yield better classification results, as well as the use of strategies to deal with potential class imbalance problems. In addition, the construction of user interfaces/dashboards designed to be utilized by health-care professionals would simplify the introduction of ML aiding tools in day-to-day clinical activities. From the visual verification of the metastatistics results, it is possible to conclude that there is some heterogeneity between studies. The log DOR forest plot displays the highest statistical heterogeneity, followed by the sensitivity and specificity ones, with the latter showing a larger number of confidence intervals overlapping. This factor is also reflected in the SROC curve since slight scattering is visible. This variability may partially be caused by the comparison of studies that implement different learners for the classification of distinct pathologies. Nonetheless, there is a good distribution of studies on the top left corner of the SROC curve, showing a good balance between sensitivity measurements and false positive rate, with the majority of SN and SP values at the highest range of the scale (SN \(>\) 0.82, SP \(>\) 0.76). Thus, the interest of ML models for biomedical thermal applications is indicated. The low \(\chi\)2 value attained for diabetic foot studies, with a high \(p\)-value, reveals that heterogeneity is insignificant (Appendix B). This fact, allied to the good sensitivity and specificity of the encountered estimators, leads to the conclusion that the use of biofthermal data for diabetic foot assessment with artificial intelligence learners could be an interesting diagnostic aiding tool.