AIdentifyAGE Ontology for Decision Support in Forensic Dental Age Assessment
arXiv:2602.16714v1 Announce Type: new Abstract: Age assessment is crucial in forensic and judicial decision-making, particularly in cases involving undocumented individuals and unaccompanied minors, where legal thresholds determine access to protection, healthcare, and judicial procedures. Dental age assessment is widely recognized as one of the most reliable biological approaches for adolescents and young adults, but current practices are challenged by methodological heterogeneity, fragmented data representation, and limited interoperability between clinical, forensic, and legal information systems. These limitations hinder transparency and reproducibility, amplified by the increasing adoption of AI- based methods. The AIdentifyAGE ontology is domain-specific and provides a standardized, semantically coherent framework, encompassing both manual and AI-assisted forensic dental age assessment workflows, and enabling traceable linkage between observations, methods, reference data, and r
arXiv:2602.16714v1 Announce Type: new Abstract: Age assessment is crucial in forensic and judicial decision-making, particularly in cases involving undocumented individuals and unaccompanied minors, where legal thresholds determine access to protection, healthcare, and judicial procedures. Dental age assessment is widely recognized as one of the most reliable biological approaches for adolescents and young adults, but current practices are challenged by methodological heterogeneity, fragmented data representation, and limited interoperability between clinical, forensic, and legal information systems. These limitations hinder transparency and reproducibility, amplified by the increasing adoption of AI- based methods. The AIdentifyAGE ontology is domain-specific and provides a standardized, semantically coherent framework, encompassing both manual and AI-assisted forensic dental age assessment workflows, and enabling traceable linkage between observations, methods, reference data, and reported outcomes. It models the complete medico-legal workflow, integrating judicial context, individual-level information, forensic examination data, dental developmental assessment methods, radiographic imaging, statistical reference studies, and AI-based estimation methods. It is being developed together with domain experts, and it builds on upper and established biomedical, dental, and machine learning ontologies, ensuring interoperability, extensibility, and compliance with FAIR principles. The AIdentifyAGE ontology is a fundamental step to enhance consistency, transparency, and explainability, establishing a robust foundation for ontology-driven decision support systems in medico-legal and judicial contexts.
Executive Summary
The article 'AIdentifyAGE Ontology for Decision Support in Forensic Dental Age Assessment' proposes an ontology-driven framework for enhancing transparency, reproducibility, and consistency in forensic dental age assessment. The AIdentifyAGE ontology integrates judicial context, individual-level information, forensic examination data, and AI-based estimation methods, providing a standardized framework for medico-legal workflows. Developed with domain experts, the ontology ensures interoperability, extensibility, and compliance with FAIR principles. By establishing a robust foundation for decision support systems, the AIdentifyAGE ontology aims to improve explainability and consistency in judicial decision-making, particularly in cases involving undocumented individuals and unaccompanied minors.
Key Points
- ▸ The AIdentifyAGE ontology provides a standardized framework for medico-legal workflows in forensic dental age assessment.
- ▸ The ontology integrates judicial context, individual-level information, forensic examination data, and AI-based estimation methods.
- ▸ The AIdentifyAGE ontology ensures interoperability, extensibility, and compliance with FAIR principles.
Merits
Strength
The AIdentifyAGE ontology addresses the limitations of current forensic dental age assessment practices, including methodological heterogeneity and limited interoperability between clinical, forensic, and legal information systems.
Comprehensive Approach
The ontology models the complete medico-legal workflow, incorporating judicial context, individual-level information, forensic examination data, and AI-based estimation methods.
Interdisciplinary Collaboration
The AIdentifyAGE ontology is being developed in collaboration with domain experts, ensuring the incorporation of diverse perspectives and expertise.
Demerits
Limitation
The article does not provide a detailed analysis of the potential biases and errors associated with AI-based estimation methods incorporated into the AIdentifyAGE ontology.
Implementation Challenges
The successful implementation of the AIdentifyAGE ontology in medico-legal contexts may be hindered by factors such as resistance to change, lack of resources, and inadequate training.
Expert Commentary
The AIdentifyAGE ontology presents a promising solution to the challenges associated with forensic dental age assessment, particularly in cases involving undocumented individuals and unaccompanied minors. By providing a standardized framework for medico-legal workflows, the ontology has the potential to improve explainability and consistency in judicial decision-making. However, the successful implementation of the AIdentifyAGE ontology will require careful consideration of factors such as resistance to change, lack of resources, and inadequate training. Furthermore, the potential biases and errors associated with AI-based estimation methods incorporated into the ontology must be addressed. Overall, the AIdentifyAGE ontology is a significant contribution to the field of forensic dental age assessment, and its development and implementation hold promise for improving the accuracy and reliability of decision-making in medico-legal contexts.
Recommendations
- ✓ Further research is needed to address the potential biases and errors associated with AI-based estimation methods incorporated into the AIdentifyAGE ontology.
- ✓ The development and implementation of the AIdentifyAGE ontology should be accompanied by careful consideration of factors such as resistance to change, lack of resources, and inadequate training.
