Background/Objectives: Multivessel coronary artery disease (CAD) remains a challenging condition requiring multidisciplinary decision-making, particularly when determining between percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG). Recent advancements in artificial intelligence (AI), particularly generative language models like ChatGPT, present an opportunity to assist in the decision-making process. However, their ability to replicate human clinical judgment in complex scenarios, such as multivessel CAD, remains untested. Methods: The aim of this study was to evaluate the concordance between recommendations from AI (ChatGPT) and those from heart team (HT) in the management of multivessel CAD, with a focus on comparing treatment strategies such as PCI and CABG. A retrospective observational study was conducted on 137 patients with multivessel CAD, discussed at multidisciplinary HT meetings in 2024. Standardized clinical vignettes, including clinical and anatomical data, were presented to ChatGPT for treatment recommendations. The AI’s responses were compared with the HT’s decisions regarding PCI or CABG. Statistical analysis was performed to assess the level of agreement and predictive value of ChatGPT’s recommendations. Results: ChatGPT achieved an overall accuracy of 65% in its recommendations. The agreement rate was higher for CABG (82.4%) than for PCI (44.4%). Discordance was identified in 48 patients, with a notable bias towards recommending CABG. Factors such as age, diabetes, and chronic kidney disease were predictors of discordance, although no significant factors emerged for the PCI or CABG subgroups. Conclusions: AI, particularly ChatGPT, demonstrated modest concordance with HT decisions in the management of multivessel CAD, especially favoring CABG. While AI offers potential as a decision-support tool, its current limitations highlight the continued need for human clinical judgment in complex cases. Further research is required to optimize AI integration into clinical decision-making frameworks.

Background: Atrial fibrillation (AF) is the most common heart arrhythmia, and its prevalence increases with age. Oral Anticoagulant Therapy (OAT) with non-vitamin K antagonist oral anticoagulants (NOACs) or vitamin K antagonists (VKAs) is essential to avoid thromboembolic events in AF. However, this treatment is associated with a high risk of bleeding and low adherence in elderly patients. Aim: The aim was to evaluate the real-world use of OAT in a population of patients aged ≥80 years in twenty-three Italian centers and to investigate the tolerance of and patient satisfaction with this therapy. Methods: The ISNEP Study is a multicenter cross-sectional study enrolling patients with AF and aged ≥80 years and treated with either NOACs or VKAs. A written questionnaire was administered to each patient to evaluate the adherence to and patient satisfaction with this therapy. Results: The study included 641 patients with a mean age of 85 (82–87) years. The use of NOACs was reported in 93.0% of cases, with the remaining 7.0% treated with VKAs. A history of stroke events was reported in five (11.1%) and one (0.2%) patients in the VKA and NOAC groups, respectively. The rate of referred ecchymosis/epistaxis was significantly higher in the VKA group compared to the NOAC group (p < 0.001). Patients receiving NOACs reported a substantial improvement in their quality of life compared to the VKA group. Conclusions: A small, but not negligible, proportion of elderly AF patients is still treated with VKAs. Patients treated with NOAC have a higher level of satisfaction with the therapy and complete adherence.