As a forensic DNA analyst, I want to prioritize DNA samples based on the likelihood of yielding useful profiles, so that I can optimize laboratory resources
Quality Assessment: AI could evaluate sample quality (e.g., degradation, contamination levels) based on metadata, laboratory notes, and initial tests.
Profile Success Prediction: AI could predict the likelihood of generating a usable DNA profile from a sample based on historical data and environmental conditions.
Case Context Integration: AI could consider the relevance of a sample to the case, such as its connection to suspects, locations, or timelines.
Priority Scoring: AI could rank samples based on a composite score that factors in quality, relevance, and case importance.
Workflow Optimization: AI could allocate resources dynamically, adjusting priorities as new samples or case information becomes available.
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.
