Cardiovascular disease remains the world’s leading cause of death, and even when patients reach guideline low-density lipoprotein cholesterol targets, a substantial “residual risk” persists, underscoring the need for more nuanced assessment and intervention. At the same time, rapid advances in high-resolution lipidomics, connected point-of-care diagnostics, and RNA- or gene-based lipid-modifying therapies are transforming what clinicians can measure, monitor, and treat. Integrating multimodal data through machine learning algorithms capable of handling high-dimensional datasets has the potential to improve cardiovascular risk prediction and re-stratification compared to traditional models. This narrative review therefore sets out to (i) trace how these emerging technologies expand our understanding of dyslipidemia beyond the traditional lipid panel, (ii) examine their potential to enable earlier, more personalized and durable cardiovascular risk reduction, and (iii) highlight the scientific, regulatory and ethical hurdles that must be cleared before such innovations can deliver widespread, equitable benefit.

Pulmonary embolism represents a major cause of morbidity and mortality worldwide, and remains a diagnostic challenge due to its highly non-specific clinical presentation. Early recognition is critical because timely diagnosis not only improves prognosis but also guides risk stratification, which is essential for therapeutic decision-making. Non-invasive diagnostic modalities – including clinical prediction rules, biomarkers, imaging techniques and bedside assessments – have become indispensable tools for rapid and accurate identification of patients with suspected pulmonary embolism. These methods enable clinicians to stratify risk, predict outcomes and tailor treatment strategies to individual patients, reducing both underdiagnosis and overtreatment. Despite their significant impact, limitations persist, such as access disparities, overdiagnosis of subsegmental events and interpretation challenges in special populations. Ongoing advances, including artificial intelligence and novel biomarkers, hold promise for refining diagnostic accuracy and personalised risk assessment. Ultimately, the integration of non-invasive tests into structured algorithms ensures earlier detection, better prognostic evaluation and improved clinical outcomes for patients with acute pulmonary embolism.