Management of infective endocarditis (IE) is often confounded by the presence of prosthetic valves, transcatheter prostheses, and cardiovascular implantable electronic devices, as well as by changing epidemiologic risks and antimicrobial resistance patterns. For the diagnosis of IE, blood cultures and echocardiography remain foundational, but important gaps persist, particularly in culture-negative and prosthetic/device-associated infections. Contemporary diagnostic criteria and guidelines now support a multimodality approach incorporating molecular microbiology and advanced imaging. This review summarizes emerging technologies including broad-range 16S/18S/28S rRNA polymerase chain reaction and sequencing, fluorescence in situ hybridization coupled with sequencing, targeted and shotgun metagenomic next-generation sequencing (mNGS), plasma microbial cell-free DNA, cardiac computed tomography, and 18F-fluorodeoxyglucose positron-emission tomography/computed tomography. We also examine the potential role of artificial intelligence in echocardiographic interpretation and of large language models in multimodal data integration. Current evidence suggests that tissue-based molecular testing generally provides greater microbiologic yield than valve culture in culture-negative IE, while blood-based mNGS and microbial cell-free DNA may extend pathogen detection after antibiotic exposure and reduce reliance on tissue-based diagnosis. However, heterogeneity in specimen handling, sequencing pipelines, contamination control, reporting thresholds, and clinical interpretation remains a major barrier to implementation. Future progress should move beyond pathogen detection toward rapid, reproducible, clinically actionable diagnosis that integrates organism identification, anatomic localization, disseminated disease, and personalized treatment. Prospective outcome trials, standardized workflows, cost-effectiveness studies, and multidisciplinary Endocarditis Team integration can help determine whether these technologies translate into better antimicrobial stewardship, procedural decision-making, and patient outcomes.
Artificial intelligence (AI) may improve electrocardiographic detection of ST-elevation myocardial infarction (STEMI), but available studies address distinct diagnostic questions. We systematically reviewed studies evaluating AI-based STEMI detection from 12-lead electrocardiograms and separately synthesized AI performance against a clinical reference standard, AI and clinician performance against a clinical reference, and AI agreement with physician electrocardiogram (ECG) interpretation. PubMed, Embase, Web of Science, Scopus, Cochrane Library, and ClinicalTrials.gov were searched from January 2010 through March 10, 2025. Bivariate random-effects models generated pooled sensitivity, specificity, and hierarchical summary receiver operating characteristic area under the curve (AUC). Twenty-six studies comprising 539,876 ECGs were included. In 20 studies comparing AI with a clinical reference standard, pooled sensitivity was 0.95 (95% confidence interval [CI], 0.91–0.97), specificity 0.95 (0.90–0.98), AUC 0.98 (0.97–0.99), positive likelihood ratio 19.2 (9.3–39.5), and negative likelihood ratio 0.05 (0.03–0.10). In 8 studies reporting both AI and clinician performance against a clinical reference, separate syntheses showed sensitivity of 0.91 (0.86–0.94) for AI versus 0.78 (0.69–0.85) for clinicians, specificity of 0.91 (0.82–0.96) for both, and AUCs of 0.95 and 0.90, respectively. In 7 studies using physician ECG interpretation as the comparator, AI sensitivity was 0.85 (0.70–0.94), specificity 0.99 (0.96–1.00), and AUC 0.98 (0.96–0.99). Sensitivity analyses excluding case–control designs, influential studies, and restricting analyses to deep-learning models or raw-waveform ECGs produced similar findings.
Preeclampsia is a multisystem hypertensive disorder of pregnancy that is defined by new-onset hypertension after 20 weeks of gestation along with proteinuria or evidence of end-organ dysfunction. Affecting 2–8% of pregnancies worldwide, preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality. This disorder originates secondary to defective placentation during the first trimester, causing incomplete remodeling of the uterine spiral arteries, resulting in placental hypoperfusion, oxidative stress, and the release of anti-angiogenic factors into the maternal circulation. This pathophysiology provided researchers a rationale for the use of low-dose aspirin as a preventive strategy for preeclampsia. Early studies investigated low-dose aspirin initiated at variable gestational ages and failed to show significant reductions in the incidence of preeclampsia. However, over the years, multiple meta-analyses studying aspirin use for preeclampsia prevention revealed a consistent decrease in the incidence of preeclampsia with the use of low-dose aspirin initiated at or before 16 weeks of gestation, with the efficacy being largely dependent on the time of initiation. In 2017, a randomized controlled trial demonstrated a statistically and clinically significant reduction in the incidence of preeclampsia leading to preterm delivery with aspirin initiated before 16 weeks gestation in women identified as high risk by first-trimester algorithm-based screening. Based on the strong evidence in support of aspirin use for preeclampsia prevention, current guidelines recommend initiation of aspirin 81 mg after 12 weeks and ideally before 16 weeks for all pregnant women at increased risk for preeclampsia.
