AI-Augmented Forensic Intelligence: Dual-Stream Deep Learning and Predictive Analytics for Integrated Toxicological Diagnosis and Criminal Profiling

Background: Forensic investigations increasingly involve complex interactions between novel psychoactive substances (NPS), multidimensional toxicological evidence, and offender behavior, yet current workflows remain siloed between laboratory toxicology and criminal profiling. Rapid evolution of “designer drugs” also challenges conventional spectral libraries and leads to delayed or inconclusive diagnoses in high‑stakes criminal cases. ​Objective: This conceptual methodology paper proposes an integrated, dual‑stream artificial intelligence (AI) framework that fuses computational toxicology and behavioral predictive modeling to support complex toxicological diagnosis and criminal profiling in a unified decision‑support system. ​Methods: Stream A applies one‑dimensional convolutional neural networks (1D‑CNNs) to gas and liquid chromatography–mass spectrometry (GC–MS/LC–MS) spectrograms from established libraries (e.g., SWGDRUG) and internal case files to detect known and emerging NPS, treating spectra as “molecular fingerprints” that can generalize to unseen analogues. Stream B employs ensemble machine‑learning models (Random Forest and gradient boosting) on structured offender‑level data, including modus operandi, victimology, scene characteristics, and toxicology results, to derive an “Offender Risk Category” and aggression‑related risk scores. Model development relies on stratified k‑fold cross‑validation, calibration assessment, and explainable AI (SHAP values) to ensure transparency suitable for judicial scrutiny. ​Results (conceptual): The proposed framework is designed to deliver: (1) automated toxicological triage that prioritizes cases with aggression‑inducing or incapacitating substances; (2) probabilistic classification of unknown substances into pharmacological classes (e.g., synthetic stimulants or opioids) even when exact molecules are absent from reference libraries; and (3) an integrated risk score (0–100) quantifying the likelihood that observed crime scene behavior aligns with substance‑driven aggression rather than purely premeditated violence 3. ​Conclusion: This dual‑stream “AI‑augmented forensics” paradigm operationalizes forensic intelligence by bridging the gap between biological toxicology and criminal profiling while embedding explainability, auditability, and human‑in‑the‑loop oversight to support court‑admissible expert opinion. Future work should implement and prospectively validate this framework on multi‑jurisdictional datasets and examine its impact on turnaround time, diagnostic accuracy, and bias mitigation in forensic decision‑making.