AI breakthrough could revolutionize forensic age estimation for asylum seekers

Determining the age of living individuals remains one of forensic science's toughest challenges. Accurate assessments carry major legal and humanitarian consequences, particularly in cases involving asylum-seeking minors. Now, new research from Portugal suggests artificial intelligence could transform this process.

Palmela Pereira, a researcher at the Universidade de Lisboa, has developed an AI-driven framework to improve forensic dental age estimation. Her 2026 study combines machine learning with dental imagery to reduce subjectivity and increase reliability.

Traditional methods for estimating age from dental development often face issues like observer bias. Differences in growth patterns across populations can also lead to inconsistencies. Pereira's approach addresses these problems by using convolutional neural networks (CNNs) to analyse key dental features. The AI examines tooth mineralisation stages, root translucency, and pulp chamber size to generate probabilistic age ranges.

The system does more than automate assessments—it provides confidence metrics for each prediction. This allows forensic experts to evaluate reliability before making decisions. The adaptable nature of AI also means the model can recalibrate based on regional demographic data, improving accuracy across diverse groups.

Beyond technical improvements, Pereira's work introduces greater transparency to a traditionally subjective field. Ethical considerations remain central, with the framework designed to ensure replicability. The implications stretch beyond forensics, particularly in humanitarian contexts where age verification affects asylum claims.

Automation through AI could also ease the workload on forensic teams. By handling preliminary assessments, the technology speeds up workflows and reduces backlogs in legal and humanitarian cases.

Pereira's AI framework offers a data-driven alternative to conventional dental age estimation. The method combines machine learning with forensic expertise to improve precision and fairness. As the system continues to develop, its impact may extend to legal proceedings, migration policies, and global forensic standards.