Background/Objectives: Malignant pleural effusions (MPEs) pose a significant challenge in clinical practice and exert a considerable socio-economic burden on the healthcare system, affecting approximately 1 million individuals annually. These effusions are a leading cause of debilitating dyspnea and a diminished quality of life among cancer patients, with distant metastasis to the pleural layers occurring in about 20% of cases during treatment. Methods: A cross-sectional, observational case-control study was conducted on 151 Bulgarian patients with a hydrothorax. The control group included 72 patients with benign diseases, confirmed via biopsy, with 38 having inflammatory and 34 non-inflammatory pleural effusions. The other 79 patients had malignant pleural involvement. These groups are representative of the main types of pleural pathology. Results: The study found that all of the tumor markers, except for PIVKA-II (Protein induced by vitamin K absence-II), showed statistically significant differences between the malignant and non-malignant patient groups, with CAE (carcinoembryonic antigen) and CA19-9 showing the most notable differences. The Receiver Operating Characteristic (ROC) analysis revealed that CA72-4 had the best ability to distinguish between the two groups, while PIVKA was the weakest, with optimal cut-off values for all of the relevant tumor markers being derived using the Youden index. Conclusions: In conclusion, our study highlights the transformative potential of pleural fluid tumor markers as precise and minimally invasive resources for distinguishing malignant from non-malignant pleural effusions. These findings pave the way for improved diagnostic accuracy and personalized clinical management, addressing a critical gap in the care of patients with pleural pathologies.

Background: Esophagectomy remains a technically demanding oncologic procedure with substantial morbidity, despite ongoing advances in minimally invasive and robotic techniques. Limitations in intraoperative visualization and anatomical recognition contribute to complications such as nerve injury and bleeding. Artificial intelligence (AI)-based intraoperative video analysis has emerged as a potential adjunct to enhance surgical perception and safety, but its application in esophagectomy has not been comprehensively reviewed. Methods: A systematic review was conducted in accordance with PRISMA guidelines. PubMed, Scopus, and Web of Science were searched without a lower date limit to identify eligible studies published up to January 2026, capturing early and contemporary applications of intraoperative AI in esophagectomy. Human studies involving any surgical approach were included. Data on the AI task, methodology, validation strategy, performance metrics, and reported clinical outcomes was extracted. Risk of bias was assessed using the ROBINS-I tool. Results: Six studies met the inclusion criteria, predominantly evaluating AI-driven analysis of intraoperative video during minimally invasive or robotic esophagectomy. Reported applications included real-time anatomical structure recognition, recurrent laryngeal nerve segmentation, detection of excessive nerve traction, instrument and event recognition, and surgical phase identification. Across studies, AI systems demonstrated performance comparable to expert surgeons for selected tasks and achieved real-time or near–real-time inference. One study reported earlier detection of excessive recurrent laryngeal nerve traction compared to conventional nerve integrity monitoring. However, most studies were retrospective, single-center, and feasibility-focused, with limited external validation and minimal assessment of patient-centered clinical outcomes. Conclusions: Artificial intelligence-based intraoperative analysis in esophagectomy is increasingly achievable and may enhance anatomical recognition, intraoperative risk detection, and procedural awareness. Nevertheless, current evidence remains preliminary, heterogeneous, and largely exploratory. Prospective, multicenter studies with standardized reporting and clinically meaningful outcome evaluation are required before routine implementation. Until such data is available, AI should be regarded as a complementary intraoperative tool rather than a standalone clinical decision-making system.