As a forensic drug analyst, I want to separate evidence items into unique populations based on characteristics, so that I can develop an efficient initial analytical scheme, prioritize testing, and ensure representative sampling for forensic analysis.
Automated Image & Texture Analysis: AI could use computer vision to analyze packaging, colors, shapes, and markings to classify evidence into distinct groups.
Spectral & Chemical Profile Clustering: AI could examine spectroscopic and chromatographic data to detect similarities and separate substances accordingly.
Machine Learning for Pattern Recognition: AI could analyze historical forensic data to identify common drug formulations, mixtures, or counterfeit trends for grouping.
Predictive Sampling Optimization: AI could determine the most statistically representative samples from each population to minimize unnecessary testing.
Dynamic Prioritization for Testing: AI could rank evidence groups by likelihood of containing high-risk substances (e.g., fentanyl-laced drugs) for targeted analysis.
Our mission is to inspire the forensic science community to explore how AI can transform their practice—enhancing reliability, efficiency, and decision-making while valuing transparency and trust
Transforming industries by delivering innovative AI solutions that drive success and create value for businesses of all sizes.
Transforming industries by delivering innovative AI solutions that drive success and create value for businesses of all sizes.
Transforming industries by delivering innovative AI solutions that drive success and create value for businesses of all sizes.
Transforming industries by delivering innovative AI solutions that drive success and create value for businesses of all sizes.
