## 4 Discussion and Conclusions  
In this article, we have reviewed some of the main works applying AI techniques to different biomedical image modalities (mainly X-ray images, CTs and MRIs, but also 3D surface scans of PM materials, i.e., bone remains) with the purpose of contributing to forensic human identification of deceased and living individuals. SFI is one of the main tools at our disposal when techniques like DNA analysis or fingerprint comparison cannot be applied, because there is no second sample to compare with or because the materials are so degraded that soft tissues are not preserved, among other reasons. AI techniques have been applied with remarkable success in many challenging tasks, including healthcare and medical imaging. From this point of view, it is highly surprising the residual presence that AI currently has in forensic anthropologists' daily practice. Even if some integral tools start to arise [212], to date forensic experts have no AI-based tools available to automate SFI tasks. Some of the solutions reviewed in this manuscript employ CV, SC and ML techniques to automate identification techniques such as CFS or CR, through the unbiased and accurate processing, analysis and comparison of AM data and PM data. Moreover, some of the presented solutions would enable fast multiple comparisons, offering efficient filtering tools that can largely reduce the candidates of a database in minutes instead of days. On the other hand, decisions could be supported on objective and reproducible results that, perhaps, would have a stronger impact in a court of law. The studies cited in this manuscript show that AI techniques, like neural networks, can be trained to estimate the BP, describe trauma- or pathological conditions, of an individual from skeletal remains or radiographic images. Other techniques are able to contribute to automatically perform the visual comparison of anatomical structures with high accuracy and have the potential to remove human bias in all these tasks. We identify a set of limitations and opportunities in the field of AI-based solutions to FA problems involving biomedical images. Regarding the **limitations**, we could mention the following:  
* The reduced number of multidisciplinary research groups including forensic researchers in anthropology and related sciences (odontology, forensic medicine or anatomy) and AI experts. This interaction is essential in order to establish a fluid and productive collaboration between different scientific disciplines. On one hand, it allows unifying terminology and facilitating the useful transfer of knowledge among scientists. On the other hand, it allows joining forces in a common direction towards challenging research projects. This interdisciplinary collaboration should bear fruit in the recognition that machines are not here to replace human beings, but to assist them and facilitate their work in those tasks that humans do not want perform or want to perform faster and easier. * With few and commendable exceptions [213; 214], there is a lack of large and open public data sets for research purposes in FA. Currently, data availability is the starting point and requirement for the validation and comparison of many proposed AI techniques (like DL approaches). This is specially true nowadays, in the DL era; since we need to remark one of the main characteristics, and at the same time limitations, of ML models (largely used nowadays in many of the described tasks, like classification, regression, or segmentation): the result will be as good as the data available, so if the dataset used to train these systems is not broad and diverse enough, the results will surely be suboptimal. Furthermore, to compare the performance of newly developed methods, a common forensic dataset of known case studies should be available.