Background: Artificial intelligence (AI) has emerged as a transformative technology in healthcare, with its integration into cardiac surgery offering significant advancements in precision, efficiency, and patient outcomes. However, a comprehensive understanding of AI’s applications, benefits, challenges, and future directions in cardiac surgery is needed to inform its safe and effective implementation. Methods: A systematic review was conducted following PRISMA guidelines. Literature searches were performed in PubMed, Scopus, Cochrane Library, Google Scholar, and Web of Science, covering publications from January 2000 to November 2024. Studies focusing on AI applications in cardiac surgery, including risk stratification, surgical planning, intraoperative guidance, and postoperative management, were included. Data extraction and quality assessment were conducted using standardized tools, and findings were synthesized narratively. Results: A total of 121 studies were included in this review. AI demonstrated superior predictive capabilities in risk stratification, with machine learning models outperforming traditional scoring systems in mortality and complication prediction. Robotic-assisted systems enhanced surgical precision and minimized trauma, while computer vision and augmented cognition improved intraoperative guidance. Postoperative AI applications showed potential in predicting complications, supporting patient monitoring, and reducing healthcare costs. However, challenges such as data quality, validation, ethical considerations, and integration into clinical workflows remain significant barriers to widespread adoption. Conclusions: AI has the potential to revolutionize cardiac surgery by enhancing decision making, surgical accuracy, and patient outcomes. Addressing limitations related to data quality, bias, validation, and regulatory frameworks is essential for its safe and effective implementation. Future research should focus on interdisciplinary collaboration, robust testing, and the development of ethical and transparent AI systems to ensure equitable and sustainable advancements in cardiac surgery.

Sentinel lymph node biopsy (SLNB) has become the standard of care for clinically node-negative breast cancer and has recently been shown by clinical trials to be also feasible for clinically node-positive patients treated with primary systemic therapy. The dual technique using both radioisotope (RI) and blue dye (BD) as tracers for the identification of sentinel lymph nodes is considered the gold standard. However, allergic reactions to blue dye as well as logistics issues related to the use of radioactive agents, have led to research on new sentinel lymph node (SLN) tracers and to the development and introduction of novel techniques in the clinical practice. Indocyanine green (ICG) is a water-soluble dye with fluorescent properties in the near-infrared (NIR) spectrum. ICG has been shown to be safe and effective as a tracer during SLNB for breast cancer and accumulating evidence suggests that ICG is superior to BD and at least comparable to RI alone and to RI combined with BD. Thus, ICG was recently proposed as a reliable SLN tracer in some breast cancer clinical practice guidelines. Nevertheless, there is lack of consensus regarding the optimal role of ICG for SLN mapping. Specifically, it is yet to be determined whether ICG should be used in addition to BD and/or RI, or if ICG could potentially replace these long-established traditional SLN tracers. This article is an updated overview of somerecent studies that compared ICG with BD and/or RI regarding their accuracy and effectiveness during SLNB for breast cancer.

Introduction: The incidence of tuberculosis is rising worldwide and rare manifestations of the past are seen more often nowadays. Mammary tuberculosis is a rare clinical entity, often mimicking breast cancer or abscesses of benign or malignant origin. Clinical awareness is necessary during diagnostic work-up for establishing the correct diagnosis and treatment.Case presentation: We present a case of breast tuberculosis diagnosed in a 73 year old woman at our institution. The patient presented with a palpable mass of the right breast with clinical, laboratory and mammographic findings indicative of breast carcinoma. The patient underwent lumpectomy and sentinel lymph node biopsy. Frozen section of the tumor and the sentinel node revealed "granulomatous inflammation", while gross examination confirmed the diagnosis of tuberculous mastitis. The patient received anti-tuberculosis therapy for six months with no side effects or any further complications.Conclusion: Breast tuberculosis is an obscure disease often mistaken for carcinoma or pyogenic abscess of the breast, especially if well-defined clinical features are absent. A high index of suspicion is required because the disease can usually be treated conservatively with current antituberculous modalities while surgical intervention is reserved for rare cases only.

Hepatocellular carcinoma (HCC) ranks among the five most common cancer entities worldwide and leads to hundred-thousands of deaths every year. Despite some groundbreaking therapeutical revelations during the last years, the overall prognosis remains poor. Although the immune system fights malignant transformations with a robust anti-tumor response, certain immune mediators have also been shown to promote cancer development. For example, interleukin (IL)-22 has been associated with HCC progression and worsened prognosis in multiple studies. However, the underlying mechanisms of the pathological role of IL-22-signaling as well as the role of its natural antagonist IL-22 binding protein (IL-22BP) in HCC remain elusive. Here, we corroborate the pathogenic role of IL-22 in HCC by taking advantage of two mouse models. Moreover, we observed a protective role of IL-22BP during liver carcinogenesis. While IL-22 was mainly produced by CD4⁺ T cells in HCC, IL-22BP was abundantly expressed by neutrophils during liver carcinogenesis. Hepatocytes could be identified as a major target of this pathological IL-22-signaling. Moreover, abrogation of IL-22 signaling in hepatocytes in IL22ra1^flox/flox × Alb^Cre+ mice reduced STEAP4 expression-a known oncogene-in HCC in vivo. Likewise, STEAP4 expression correlated with IL22 levels in human HCC samples, but not in healthy liver specimens. In conclusion, these data encourage the development of therapeutical approaches that target the IL-22–IL-22BP axis in HCC.

Liver metastases are rare in salivary gland tumors and have been reported only once to be the first manifestation of the disease. They are usually treated with surgical resection of the primary tumor and systemic chemotherapy. Drug-eluting bead chemoembolization has an evolving role in the treatment of hepatocellular carcinoma, as well as in the treatment of metastatic disease of the liver. Nevertheless, it has never been used in a patient with salivary gland liver metastases.

Over the last decades, the field of medicine has witnessed significant progress in artificial intelligence (AI), the Internet of Medical Things (IoMT), and deep learning (DL) systems. Otorhinolaryngology, and imaging in its various subspecialties, has not remained untouched by this transformative trend. As the medical landscape evolves, the integration of these technologies becomes imperative in augmenting patient care, fostering innovation, and actively participating in the ever-evolving synergy between computer vision techniques in otorhinolaryngology and AI. To that end, we conducted a thorough search on MEDLINE for papers published until June 2023, utilizing the keywords ‘otorhinolaryngology’, ‘imaging’, ‘computer vision’, ‘artificial intelligence’, and ‘deep learning’, and at the same time conducted manual searching in the references section of the articles included in our manuscript. Our search culminated in the retrieval of 121 related articles, which were subsequently subdivided into the following categories: imaging in head and neck, otology, and rhinology. Our objective is to provide a comprehensive introduction to this burgeoning field, tailored for both experienced specialists and aspiring residents in the domain of deep learning algorithms in imaging techniques in otorhinolaryngology.