# Artificial Intelligence and Forensic Genetics: Current Applications and Future Perspectives
## 4 Discussion
[PERSON] and [PERSON] [84] analyzed the possibility of applying an algorithm to fingerprint patterns, which are determined by a combination of genetic and environmental factors, to determine gender and age group, and in predicting blood group and classifying common clinical diseases that have a genetic basis, including hypertension, type-2 diabetes, and arthritis. After all, electronic fingerprint recognition has been commercially applied with identification purposes and could be translated to the forensic field [85]. Indeed, to date, the use of fingerprints for identification purposes in the forensic field is one of the more affordable methods, even though recognizing fragmented fingerprints is a difficulty for forensic experts; in this way, the development of AI represents an important tool [86]. Moreover, in a recent study the main goal of the authors was to calculate the sexual dimorphism in unfused or disarticulated hyoids, using ML methods [87]. AI and forensic genetics have emerged as two rapidly evolving fields that are revolutionizing the way investigations are conducted in various domains, especially in the field of criminal justice. Considering the paucity of research, to improve the AI application in forensic genetics, there is a need for interdisciplinary collaboration between AI researchers, forensic scientists, and legal experts to ensure the responsible and ethical implementation of these technologies in the criminal justice system. AI may improve the accuracy of DNA profiling, predicting physical traits and ancestry from DNA samples, and identifying potential familial relationships among individuals [88]. Interestingly, there is a possibility of exploring the use of AI in data interpretation in the field of DNA transfer. DNA transfer in forensic investigation is a crucial process: the sensitivity and specificity of DNA analysis techniques have greatly improved over the years, allowing investigators to detect and analyze even the smallest traces of DNA that could be transferred in a direct/indirect manner [89; 90; 91; 92; 93]. The ability to transfer DNA from one surface to another unintentionally, referred to as secondary or indirect transfer, can occur through a variety of mechanisms, such as direct contact or environmental factors [94; 95]. Forensic investigators must carefully consider the context of DNA findings, evaluating the likelihood of direct or indirect transfer, and cross-checking various pieces of evidence to ensure accuracy in their conclusions [96]. Of particular interest is indirect transfer that occurs when DNA is transferred between individuals or objects indirectly, often through third-party or environmental factors. One common example is the transfer of DNA from an object to a person and then onto another object [97; 98; 99]. Factors such as the duration of contact, intensity of contact, and the nature of the surface can affect the presence and persistence of DNA [100; 101]. For instance, if an individual handles a weapon that is later used in a crime, their DNA may be transferred from the weapon onto the perpetrator, providing a link between the suspect and the crime scene. DNA can be carried through the air by particles such as dust, dander, or skin cells, settling onto surfaces and ultimately being transferred to other objects or individuals [102]. It is crucial for forensic investigators to consider these mechanisms and recognize potential sources of indirect transfer when analyzing DNA evidence. Understanding the principles of DNA transfer (i.e., transfer, persistence, prevalence and recovery of DNA) is essential for forensic investigators to accurately interpret DNA evidence and present reliable conclusions in a court of law [100; 103]. The Bayesian model has become widely used in evaluating indirect DNA transfer in the forensic field, providing a statistical framework for assessing the likelihood of such transfers. One key aspect of the Bayesian model is the use of prior probabilities: these represent the initial beliefs about the likelihood of different scenarios.