A prospective study found that an AI-enhanced 12-lead ECG score accurately identified angiographically significant coronary artery disease in patients undergoing elective angiography. The results suggest AI can extract clinically relevant patterns from standard ECG inputs, potentially enabling earlier triage without new imaging or invasive testing. The study positions AI ECG as a scalable screening and risk-stratification tool, but the core value proposition hinges on prospective deployment and consistent performance across clinical settings. For cardiometabolic pipeline and diagnostics teams, it reinforces the relevance of algorithmic ECG as a front-line digital diagnostic layer—especially as healthcare systems look for opportunistic detection in routine workflows.