## 4 Discussion and Conclusions
This is a common practice in other fields as close as clinical medicine and biomedical research, with representative examples such as classification and localization of common thorax diseases in chest X-ray [215], melanoma classification in skin photographs [216], the cancer imaging archive [217], the Allen mouse and human brain atlases for combining genomics and neuroanatomy [218], the MIMIC critical care database that contains health data associated with approximately 40,000 critical care patients [219], the Alzheimer's Disease neuroimaging initiative [220], or the OpenNeuro initiative for sharing MRI and fMRI data [221], among others. It is also important to recognize the fact that it is not only about acquiring and storing data, but how these data are compiled in order to meet ethical requirements and avoid machine bias [222], among others. All this problematic, related to the absence of large and complete datasets, may also be at the root of the inadequacy found in how some methods calculate, express and interpret the obtained errors (for instance, in age estimation [223]). Associated with those limitations, we find the following **opportunities** in this research field:
* Many of today's most popular and successful AI techniques to tackle imaging problems are rarely used in FA applications. This fact will undoubtedly open a niche for its application. We can think about techniques of few-shot learning [224] (to train models from a few examples), generative adversarial networks [225] or non-adversarial generative models [226] (for example, to generate data when these are scarce), or Siamese networks [227] (to establish if two images provided as input belong to the same class). Similarly, the use of 3D ConvNets [228] to directly process three-dimensional information is non-existent, as well as the combined use of recurrent and convolutional networks [229] to carry out more complex tasks (such as the textual description of images automatically, i.e., image captioning). Even the automation of certain tasks, like landmarking [230] or head pose estimation [231], is seldom performed using state-of-the-art ML/CV approaches. * As in many other medical applications, the accuracy of the results is not the only goal to be accomplished. The results are also intended to be understandable by human users. The recently introduced concept of explainable AI encompasses AI systems for opening black box models,for improving the understanding and comprehensibility of what the models have learned, and/or for explaining individual predictions [232; 233]. FA demands solutions designed considering the required explainability of some particular tasks while achieving the required performance and accuracy, providing the designed (human-centric) models or decision support systems with the ability to trust the system output [234]. These types of approaches are very scarce within the research topic addressed in this article. However, its introduction into the field would be highly desirable, due to the necessity for the conclusions of a FA report to be understood by a medical examiner or a judge. We end this article with some of the specific activities or interventions that we consider preferential in order to fill the technological gap in SFI. These represent a list of **future works** in the fields of CFS, CR and BP, the three main forensic identification methodologies addressed in this work:  
* In relation to CFS, it would be especially relevant to carry out systematic and massive studies to verify its effectiveness as a forensic identification technique. AI techniques would be very useful in carrying out massive experiments and in automating and objectifying the sub-tasks involved in the CFS process. All this would contribute to increasing the scientific support of CFS, as well as considering CFS as a primary identification technique (instead of secondary) (Remember that primary identification methods are those that allow positive identification of a person.