Objectives: Non-ST-elevation myocardial infarction (NSTEMI) is a common and severe condition that requires rapid and accurate risk assessment and treatment. The Naples prognostic score (NPS) is a novel risk score that integrates nutritional and inflammatory parameters. The aim of this study was to investigate the NPS as a predictor of one-year mortality in NSTEMI patients undergoing percutaneous coronary intervention (PCI). Methods: This retrospective study included 197 NSTEMI patients who underwent selective PCI from January 2020 to December 2020. The NPS was calculated based on the total cholesterol, serum albumin, neutrophil/lymphocyte ratio, and lymphocyte/monocyte ratio. Patients were categorized into two groups based on their NPS values: Group 1 (NPS 0–2) and Group 2 (NPS 3 or 4). The one-year mortality status of the patients was determined through phone calls or by querying the national death registry system. Results: During the follow-up period, the overall mortality rate was 19.3% (n = 38). The high NPS group exhibited a significantly higher mortality rate compared to the low NPS group, with rates of 33.7% and 8.1%, respectively (p < 0.001). A Cox regression analysis indicated that a high NPS score is an independent predictor of one-year mortality, with a hazard ratio of 4.52 (95% CI: 1.93–10.58; p < 0.001). Conclusions: The NPS is a simple, cheap, and easily accessible tool that can be used for risk stratification and treatment selection in NSTEMI patients. It also highlights the importance of inflammatory and nutritional status in influencing the prognosis of NSTEMI patients.

Arterial diseases (ADs) are a significant health problem, with high mortality and morbidity rates. Endovascular interventions, such as balloon angioplasty (BA), bare-metal stents (BMSs), drug-eluting stents (DESs) and drug-coated balloons (DCBs), have made significant progress in their treatments. However, the issue has not been fully resolved, with restenosis remaining a major concern. In this context, bioresorbable vascular stents (BVSs) have emerged as a promising area of investigation. This manuscript includes articles that assess the use of BVSs. Studies have identified ongoing challenges, such as negative vascular remodeling and elastic recoil post-angioplasty, stent-related injury, and in-stent restenosis following BMS placement. While DESs have mitigated these issues to a considerable extent, their durable structures are unable to prevent late stent thrombosis and delay arterial recovery. BVSs, with their lower support strength and tendency towards thicker scaffolds, increase the risk of scaffold thrombosis. Despite inconsistent study results, the superiority of BVSs over DESs has not been demonstrated in randomized trials, and DES devices continue to be the preferred choice for most cases of arterial disease. Esprit BTK (Abbott Vascular) received approval from the US FDA for below-knee lesions in 2024, offering hope for the use of BVSs in other vascular conditions. Enhancing the design and thickness of BVS scaffolds may open up new possibilities. Large-scale and longer-term comparative studies are still required. This article aims to provide an overview of the use of biodegradable stents in the endovascular treatment of vascular stenosis.

Background/Objectives: Large language models (LLMs) have shown promising results in medical decision support; however, their effectiveness in managing acute cholecystitis and other gallbladder diseases remains insufficiently examined. This study evaluated the performance of a neuro-symbolic LLM system that integrates multiple AI agents with neural–symbolic reasoning for acute cholecystitis management and compared its diagnostic accuracy with that of human expert physicians across three clinical specialties. Methods: This multi-center cross-sectional study included 30 case-based questions covering acute cholecystitis and gallbladder diseases, stratified across eight predefined disease categories: acute calculous cholecystitis (n = 6), acute acalculous cholecystitis (n = 2), complicated cholecystitis including gangrenous, emphysematous, and perforated variants (n = 5), chronic cholecystitis and biliary colic (n = 4), gallbladder polyps and adenomyomatosis (n = 3), Mirizzi syndrome (n = 2), gallbladder carcinoma (n = 4), and post-cholecystectomy complications (n = 4). Questions were categorized into diagnosis (n = 10), treatment (n = 10), and complications/prognosis (n = 10). Gold standard answers were established through consensus by an expert panel consisting of two senior general surgery expert clinicians and one senior emergency medicine expert clinician, each with more than 20 years of clinical experience, utilizing the Tokyo Guidelines 2018 (TG18) as the reference standard for diagnostic criteria, severity grading, and management recommendations. The expert panel achieved unanimous consensus on all 30 gold standard answers. All responses were cross-referenced against the primary TG18 publications to ensure guideline-based rather than solely opinion-based reference standards. This consensus-based, guideline-anchored approach is consistent with established methodologies for gold standard establishment in AI diagnostic accuracy studies. Performance of a neuro-symbolic LLM system orchestrated via LangGraph v1.0 was compared against 10 general surgery specialists, 10 emergency medicine physicians, and 10 gastroenterology specialists from four tertiary centers in Turkey. The neuro-symbolic system incorporated the Tokyo Guidelines 2018 (TG18) as its symbolic knowledge base for diagnostic criteria, severity grading, and management algorithms. Results: The neuro-symbolic system attained the highest overall accuracy rate of 96.7% (29/30), markedly surpassing the performance of general surgery specialists (average 82.3% ± 6.8%), emergency medicine physicians (average 71.0% ± 8.2%), and gastroenterology specialists (average 78.7% ± 7.4%). Furthermore, the neuro-symbolic system exhibited superior performance across all clinical categories. Among human participants, general surgeons showed the highest accuracy in treatment decisions (88.0%), while gastroenterologists excelled in diagnostic questions (82.0%). Emergency medicine physicians showed comparable performance to other specialties in acute presentation scenarios. ROC analysis revealed excellent discrimination for the neuro-symbolic system (AUC = 0.983) compared to general surgery (AUC = 0.856), gastroenterology (AUC = 0.821), and emergency medicine (AUC = 0.764). Conclusions: The neuro-symbolic LLM system exhibited superior performance in standardized guideline-concordant case-based assessment of acute cholecystitis management compared to all human expert groups, reflecting its consistent application of encoded guideline criteria. These findings support its potential role as a clinical decision-support tool that augments, rather than replaces, physician expertise. The system’s consistent application of standardized guidelines indicates its potential utility as a clinical decision support tool, particularly in settings where specialist expertise is limited. However, these results should be interpreted within the constraints of a structured case-based evaluation and do not imply global clinical superiority over human experts.

Background: Multimodal large language models (MLLMs) integrating multiple AI systems and unimodal large language models (LLMs) represent distinct approaches to clinical decision support. Their comparative performance against human clinical experts in complex cardiovascular emergencies remains inadequately characterized. Objective: To compare the performance of a combined MLLM system (GPT-4V + Med-PaLM 2 + BioGPT), a unimodal LLM (ChatGPT-5.2), and human physicians from multiple centers (radiologists, emergency medicine specialists, cardiovascular surgeons) on aortic dissection clinical questions across diagnosis, treatment, and complication management domains. Methods: This multicenter cross-sectional study was conducted across five tertiary care centers in Turkey (Elazığ, Ankara, Antalya). A total of 25 validated multiple-choice questions were categorized into three domains: diagnosis (n = 8), treatment (n = 9), and complication management (n = 8). Questions were administered to the MLLM, ChatGPT-5.2 (Unimodal), and nine physicians from five centers: radiologists (n = 3), emergency medicine specialists (n = 3), and cardiovascular surgeons (n = 3). Statistical comparisons utilized chi-square tests. Results: Overall accuracy was 92.0% for the MLLM and 96.0% for ChatGPT-5.2 (Unimodal). Among human physicians, cardiovascular surgeons achieved 96.0%, radiologists 92.0%, and emergency medicine specialists 89.3%. The MLLM excelled in diagnosis (100%) but showed lower performance in treatment (88.9%) and complication management (87.5%). No significant differences were observed between AI models and human physician groups (all p > 0.05). Conclusions: Both the MLLM and unimodal ChatGPT-5.2 demonstrated performance within the range of human clinical experts in this controlled assessment of aortic dissection scenarios, though definitive conclusions regarding equivalence require larger-scale validation. These findings support further investigation of complementary roles for different AI architectures in clinical decision support.