A dual-pathway deep learning system from Algiers reported tuberculosis drug-resistance prediction above 95% accuracy while auditing sequencing quality and recovering both known and novel resistance loci. The approach targets a practical bottleneck in resistance genomics: pipelines can silently fail or produce unreliable outputs. By building in an evidence-audit component alongside the predictive model, the work aims to reduce false confidence from sequencing artifacts and data preprocessing issues. If validated across cohorts and platforms, this could improve how labs prioritize resistance candidates and how clinicians interpret results. The next benchmark will be performance stability across different data distributions, sample qualities, and sequencing depths typical in real-world tuberculosis programs.