: OBJECTIVES: Extended reality (XR), encompassing both virtual reality (VR) and augmented reality, allows the user to interact with a computer-generated environment based on reality. In essence, the immersive nature of VR and augmented reality technology has been warmly welcomed in all aspects of medicine, gradually becoming increasingly feasible to incorporate into everyday practice. In recent years, XR has become increasingly adopted in thoracic surgery, although the extent of its applications is unclear. Here, we aim to review the current applications of XR in thoracic surgery. METHODS: A systematic database search was conducted of original articles that explored the use of VR and/or augmented reality in thoracic surgery in EMBASE, MEDLINE, Cochrane database and Google Scholar, from inception to December 2020. RESULTS: Our search yielded 1494 citations, of which 21 studies published from 2007 to 2019 were included in this review. Three main areas were identified: (i) the application of XR in thoracic surgery training; (ii) preoperative planning of thoracic procedures; and (iii) intraoperative assistance. Overall, XR could produce progression along the learning curve, enabling trainees to reach acceptable standards before performing in the operating theatre. Preoperatively, through the generation of 3D-renderings of the thoracic cavity and lung anatomy, VR increases procedural accuracy and surgical confidence through familiarization of the patient’s anatomy. XR-assisted surgery may have therapeutic use particularly for complex cases, where conventional methods would yield inadequate outcomes due to inferior accuracy. CONCLUSION: XR represents a salient step towards improving thoracic surgical training, as well as enhancing preoperative planning and intraoperative guidance.

Background: This review aims to systematically evaluate the currently available evidence investigating the use of artificial intelligence (AI) and machine learning (ML) in the field of cardiac transplantation. Furthermore, based on the challenges identified we aim to provide a series of recommendations and a knowledge base for future research in the field of ML and heart transplantation.Methods: A systematic database search was conducted of original articles that explored the use of ML and/or AI in heart transplantation in EMBASE, MEDLINE, Cochrane database, and Google Scholar, from inception to November 2021.Results: Our search yielded 237 articles, of which 13 studies were included in this review, featuring 463 850 patients. Three main areas of application were identified: (1) ML for predictive modeling of heart transplantation mortality outcomes; (2) ML in graft failure outcomes; (3) ML to aid imaging in heart transplantation. The results of the included studies suggest that AI and ML are more accurate in predicting graft failure and mortality than traditional scoring systems and conventional regression analysis. Major predictors of graft failure and mortality identified in ML models were: length of hospital stay, immunosuppressive regimen, recipient's age, congenital heart disease, and organ ischemia time. Other potential benefits include analyzing initial lab investigations and imaging, assisting a patient with medication adherence, and creating positive behavioral changes to minimize further cardiovascular risk.Conclusion: ML demonstrated promising applications for improving heart transplantation outcomes and patient‐centered care, nevertheless, there remain important limitations relating to implementing AI into everyday surgical practices.

Summary: The integration of artificial intelligence (AI) into surgery raises significant ethical concerns, including the impact on autonomy, human authority and the patient–doctor relationship. This study underscores the need for a multidisciplinary approach to navigate these ethical dilemmas, involving stakeholders from various fields. A comprehensive literature review up to March 2024 was conducted to assess the ethical implications of AI applications in surgery. This included an examination of data privacy, informed consent, algorithmic bias, the role of advanced robotics, and the impact on surgeons’ decision-making. The study also considered the development of autonomous surgical robots and their ethical implications. The review highlights that while AI can enhance surgical precision and improve clinical decision-making, it also poses several ethical challenges. AI’s ability to support decision-making risks undermining surgeons’ autonomy and judgement, raising concerns about over-reliance on technology. Issues such as data privacy, algorithmic bias and equitable access to AI-driven tools were identified as key ethical concerns. Autonomous surgical robots, while promising, introduce complex questions about accountability and liability, particularly when unexpected outcomes occur. Effective integration of AI into surgical practices demands the development of ethical frameworks that respect both the capabilities of AI and the irreplaceable value of human judgement. Balancing technological advancement with ethical integrity is essential to safeguard patient-centred care and ensure equitable access to AI benefits in healthcare.

Medical soft robotics constitutes a rapidly developing field in the treatment of cardiovascular diseases, with a promising future for millions of patients suffering from heart failure worldwide. Herein, the present state and future direction of artificial muscle‐based soft robotic biomedical devices in supporting the inotropic function of the heart are reviewed, focusing on the emerging electrothermally artificial heart muscles (AHMs). Artificial muscle powered soft robotic devices can mimic the action of complex biological systems such as heart compression and twisting. These artificial muscles possess the ability to undergo complex deformations, aiding cardiac function while maintaining a limited weight and use of space. Two very promising candidates for artificial muscles are electrothermally actuated AHMs and biohybrid actuators using living cells or tissue embedded with artificial structures. Electrothermally actuated AHMs have demonstrated superior force generation while creating the prospect for fully soft robotic actuated ventricular assist devices. This review will critically analyze the limitations of currently available devices and discuss opportunities and directions for future research. Last, the properties of the cardiac muscle are reviewed and compared with those of different materials suitable for mechanical cardiac compression.

This review aims to understand the present circumstances on the provision of prehospital trauma care in low- and middle-income countries (LMICs), particularly scoping the challenges experienced by ...

Background: Sarcopenia is a degenerative condition characterised by the loss of skeletal muscle mass and strength. Its impact on cardiac surgery outcomes remains poorly investigated. This meta-analysis aims to provide a comprehensive synthesis of the available evidence to determine the effect of sarcopenia on cardiac surgery outcomes. Methods: A systematic review and meta-analysis followed PRISMA guidelines from inception to April 2023 in EMBASE, MEDLINE, Cochrane database, and Google Scholar. Twelve studies involving 2717 patients undergoing cardiac surgery were included. Primary outcomes were early and late mortality; secondary outcomes included surgical time, infection rates, and functional outcomes. Statistical analyses were performed using appropriate methods. Results: Sarcopenic patients (906 patients) had a significantly higher risk of early mortality (OR: 2.40, 95% CI: 1.44 to 3.99, p = 0.0007) and late mortality (OR: 2.65, 95% CI: 1.57 to 4.48, p = 0.0003) compared to non-sarcopenic patients (1811 patients). There were no significant differences in overall surgical time or infection rates. However, sarcopenic patients had longer ICU stays, higher rates of renal dialysis, care home discharge, and longer intubation times. Conclusion: Sarcopenia significantly increases the risk of early and late mortality following cardiac surgery, and sarcopenic patients also experience poorer functional outcomes.