## 1 Introduction
This lack of technological development and hybridization of AI with FA is noteworthy since, among other reasons, the human ID field, to which FA belongs, has a remarkable and increasing social and economic importance: the global human ID market reached $43.0 billion in 2019 and should reach $83.9 billion by 2024 ([[https://www.bccresearch.com/market-research/biotechnology/human-identification-forensics-genealogy-and-security-applications-market-report.html](https://www.bccresearch.com/market-research/biotechnology/human-identification-forensics-genealogy-and-security-applications-market-report.html)]([https://www.bccresearch.com/market-research/biotechnology/human-identification-forensics-genealogy-and-security-applications-market-report.html](https://www.bccresearch.com/market-research/biotechnology/human-identification-forensics-genealogy-and-security-applications-market-report.html))). Very few surveys exist partly or totally focused on the application of AI techniques to particular SFI tasks and methodologies [5; 6]. In this sense, up to the authors' knowledge, this is the first paper to tackle the broad subject of AI-based biomedical image analysis for FA-based human ID. From this perspective, we will not tackle other forensic ID techniques like identification from biomolecular evidence (DNA), identification from latent prints (fingerprints, earprints), identification from methods of communication (e.g., handwriting), identification from podiatry and walking, or identification from personal effects. The important field of facial recognition and identification [7; 8] is not addressed either, despite its great importance and technological development, because facial images are not considered biomedical images (in the sense that they are not visual representations of the interior of a human body, and they are not generally acquired for diagnostic or therapeutic purposes). In addition, there already exist exhaustive surveys [9; 10; 11] focusing on human ID based on facial images. In this manuscript, we try to cover both the ID of deceased and living individuals employing hard tissues (bones) displayed using different biomedical imaging modalities (see Figure 1). The most popular biomedical image modalities employed for ID are X-ray images and CTs. The reason for this can be twofold. First, X-ray images are the most commonly acquired medical imaging modality worldwide. Just as an example, 2.02 million chest X-ray images were performed in 2015/16 by the National Health Service of United Kingdom [12], and 150 million are annually acquired in US [13]. In the forensic domain, CT imaging is increasingly being performed in forensic examinations, whereas most reports that exist on PM MRI are generally based on small case samples [14]. Second, even if PM CT and MRI have proven to be useful diagnostic tools in forensic medicine, some studies remark that CT is superior to MRI in the visualization of osseous and ligamentous injuries after trauma [15]. After an introductory section devoted to the methodological background (Section 2), the paper focuses on the SFI-based approaches that employ biomedical images (such as radiographs, CTs, MRIs, or 3D bone scans) to the ID of living and deceased individuals. In Section 3 the different families of forensic ID methods and AI-based techniques used in the literature are specified. The manuscript concludes with Section 4, where some conclusions, recommendations and future research lines are discussed.