Sodium glucose cotransporter 2 (SGLT2) inhibitors and glucagon-like-peptide-1 receptor (GLP-1-R) agonists are novel therapeutic agents used for the management of type 2 diabetes mellitus (T2DM). Recently, large-scale randomized clinical trials have been conducted to assess the cardiovascular safety of these medications. The findings of these trials have revealed that both SGLT2 inhibitors and GLP-1-R agonists exhibit favorable cardioprotective effects, including reduction in cardiovascular and all-cause mortality, a decreased risk of chronic kidney disease progression, a decrease in hospitalization for heart failure (HF), an effect shown by SGLT2 inhibitors, and stroke prevention, an effect shown by GLP-1-R agonists. Based on the results from above studies, the European and American Diabetes Associations have issued new recommendations strongly endorsing the use of SGLT2 inhibitors and GLP-1-R agonists in combination with metformin for patients with T2DM who have additional cardiovascular (CV) comorbidities or risk factors. The primary aim of this combined therapy is to prevent CV events. Although both medication groups offer beneficial effects, they demonstrate slightly different profiles. SGLT2 inhibitors have exhibited better effects regarding a reduced incidence of HF, whereas GLP-1-R agonists have shown a reduced risk of CV events, particularly stroke. Moreover, recent European Society of Cardiology as well as American College of Cardiology and American Heart Association guidelines of HF treatment stressed the importance of SGLT2 inhibitor administration in patients with HF regardless of T2DM. In this context, we present and discuss the outcomes of the most recent trials investigating the impact of SGLT2 inhibitors and GLP-1-R agonists on renal and cardiovascular outcomes in patients, both with and without T2DM. Additionally, we explore the synergistic effects of combining SGLT2 inhibitors and GLP-1-R agonists in patients with cardiovascular disease.

Aims: Artificial intelligence (AI) is increasingly being applied in healthcare, including cardiology, with growing interest in its potential to support diagnosis, prognosis, and clinical decision-making. The rapid pace of technological advancements is not matched by equivalent progress in the regulation and appraisal of these systems. This review aims to provide a structured evidence map of AI models in cardiology, by grading the robustness of clinical validation and mapping it against the quantity of publications across main subspecialties. Methods and results: To assess the strength of evidence, we defined, developed, and applied a novel classification tailored for AI models that ranks evidence into five Grades of Validation according to the level of clinical validity. We screened literature from 2014 to 2026 according to the PRISMA guidelines, identifying a total of 2807 articles on AI applications within cardiology, 490 of which met the inclusion criteria (validation was performed in at least one external, independent patient population), revealing a highly heterogeneous distribution of evidence across subspecialties. Most studies in AI focused on improved diagnostics (n = 322, 65.7%); however, advanced integration in prognostic (n = 122, 24.9%) and therapeutic applications remains limited (n = 46, 9.4%). Cardiovascular imaging leads AI applications, especially in coronary computed tomography and echocardiography, followed by non-invasive electrophysiology, predominantly in atrial fibrillation screening and ventricular dysfunction detection. Conclusion: The scarcity of prospective validations across different resources and geographical settings (n = 134, 27.3%) and randomized controlled trials (n = 35, 3.8%) limits the adoption by clinical guidelines and widespread implementation of AI algorithms.