Advancements and Challenges in the Application of Artificial Intelligence in Surgical Arena: A Literature Review

International expert consensus on the current status and future prospects of artificial intelligence in metabolic and bariatric surgery

Artificial intelligence (AI) is transforming the landscape of medicine, including surgical science and practice. The evolution of AI from rule-based systems to advanced machine learning and deep learning algorithms has opened new avenues for its application in metabolic and bariatric surgery (MBS). AI has the potential to enhance various aspects of MBS, including education and training, decision-making, procedure planning, cost and time efficiency, optimization of surgical techniques, outcome and complication prediction, patient education, and access to care. However, concerns persist regarding the reliability of AI-generated decisions and associated ethical considerations. This study aims to establish a consensus on the role of AI in MBS using a modified Delphi method. A panel of 68 leading metabolic and bariatric surgeons from 35 countries participated in this consensus-building process, providing expert insights into the integration of AI in MBS. Of the 28 statements evaluated, a consensus of at least 70% was achieved for all, with 25 statements reaching consensus in the first round and the remaining three in the second round. Experts agreed that AI has the potential to enhance the evaluation of surgical skills in MBS by providing objective, detailed assessments, enabling personalized feedback, and accelerating the learning curve. Most experts also recognized AI's role in identifying qualified candidates for MBS referrals, helping patient and procedure selection, and addressing specific clinical questions. However, concerns were raised about the potential overreliance on AI-generated recommendations. The consensus emphasized the need for ethical guidelines governing AI use and the inclusion of AI's role in decision-making within the patient consent process. Furthermore, the results suggest that AI education should become an essential component of future surgical training. Advancements in AI-driven robotics and AI-integrated genomic applications were also identified as promising developments that could significantly shape the future of MBS.

Artificial Intelligence and Robotics in Minimally Invasive and Complex Surgical Procedures: A Systematic Review

Robotics and artificial intelligence (AI) are transforming surgery by improving patient outcomes, efficiency, and precision. The use of robotics in surgery tackles important issues, including surgical precision, minimally invasive procedures, and healthcare accessibility, as global healthcare systems embrace AI-driven technologies more and more. However, access gaps and ethical issues with automation continue to exist on a worldwide scale, calling for a fair discussion of these developments. The aim of this systematic review was to summarize the most recent literature on the role of AI and robotics in minimally invasive and complex surgical procedures. The study was conducted as per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We search for relevant studies across four different databases (PubMed, Scopus, Web of Science, and Google Scholar). We also restricted our search to 2024 to capture the most recent advancement only. We found 393 relevant studies, of which only 12 were included in this study upon assessing them with inclusion and exclusion criteria. The review ensures scientific rigor and openness by evaluating surgical specializations, AI technologies, and outcomes such as accuracy, recovery, and complications. The results show significant progress in AI-powered surgical systems, enhancing judgment, lowering surgical errors, and enabling individualized treatment plans. AI-enhanced visualization, real-time data processing, and automated robotic equipment are notable innovations that boost patient safety and procedure efficiency. The importance of these developments is emphasized throughout the discussion, especially with regard to developing minimally invasive procedures and increasing surgical capabilities for complex surgeries. However, obstacles to broad adoption are cited, including expenses, moral dilemmas, and the requirement for stringent training procedures.

AI-powered techniques in anatomical imaging: Impacts on veterinary diagnostics and surgery

BACKGROUND

Artificial intelligence (AI) is rapidly transforming veterinary diagnostic imaging, offering improved accuracy, speed, and efficiency in analyzing complex anatomical structures. AI-powered systems, including deep learning and convolutional neural networks, show promise in interpreting medical images from various modalities like X-rays, ultrasounds, CT scans, and MRI/mammography.

STUDY DESIGN

Narrative review OBJECTIVE: This review aims to explore the innovations and challenges of AI-enabled imaging tools in veterinary diagnostics and surgery, highlighting their potential impact on diagnostic accuracy, surgical risk mitigation, and personalized veterinary healthcare.

METHODS

We reviewed recent literature on AI applications in veterinary diagnostic imaging, focusing on their benefits, limitations, and future directions.

CONCLUSION

AI-enabled imaging tools hold immense potential for revolutionizing veterinary diagnostics and surgery. By enhancing diagnostic accuracy, enabling precise surgical planning, and supporting personalized treatment strategies, AI can significantly improve animal health outcomes. However, addressing challenges related to data privacy, algorithm bias, and integration into clinical workflows is crucial for the widespread adoption and success of these transformative technologies.

Technological Advancements in Augmented, Mixed, and Virtual Reality Technologies for Surgery: A Systematic Review

Recent advancements in artificial intelligence (AI) have shown significant potential in the medical field, although many applications are still in the research phase. This paper provides a comprehensive review of advancements in augmented reality (AR), mixed reality (MR), and virtual reality (VR) for surgical applications from 2019 to 2024 to accelerate the transition of AI from the research to the clinical phase. This paper also provides an overview of proposed databases for further use in extended reality (XR), which includes AR, MR, and VR, as well as a summary of typical research applications involving XR in surgical practices. Additionally, this paper concludes by discussing challenges and proposed solutions for the application of XR in the medical field. Although the areas of focus and specific implementations vary among AR, MR, and VR, current trends in XR focus mainly on reducing workload and minimizing surgical errors through navigation, training, and machine learning-based visualization. Through analyzing these trends, AR and MR have greater advantages for intraoperative surgical functions, whereas VR is limited to preoperative training and surgical preparation. VR faces additional limitations, and its use has been reduced in research since the first applications of XR, which likely suggests the same will happen with further development. Nonetheless, with increased access to technology and the ability to overcome the black box problem, XR's applications in medical fields and surgery will increase to guarantee further accuracy and precision while reducing risk and workload.

Lessons learned in application driven imaging agent design for image-guided surgery

To meet the growing demand for intraoperative molecular imaging, the development of compatible imaging agents plays a crucial role. Given the unique requirements of surgical applications compared to diagnostics and therapy, maximizing translational potential necessitates distinctive imaging agent designs. For effective surgical guidance, exogenous signatures are essential and are achievable through a diverse range of imaging labels such as (radio)isotopes, fluorescent dyes, or combinations thereof. To achieve optimal in vivo utility a balanced molecular design of the tracer as a whole is required, which ensures a harmonious effect of the imaging label with the affinity and specificity (e.g., pharmacokinetics) of a pharmacophore/targeting moiety. This review outlines common design strategies and the effects of refinements in the molecular imaging agent design on the agent's pharmacological profile. This includes the optimization of affinity, pharmacokinetics (including serum binding and target mediated background), biological clearance route, the achievable signal intensity, and the effect of dosing hereon.

Advancements in Therapeutic Approaches for Proliferative Vitreoretinopathy: A Comprehensive Review

Proliferative vitreoretinopathy (PVR) is a significant complication of retinal detachment surgery, characterized by the growth of fibrous membranes that can lead to recurrent retinal detachment and vision loss. This comprehensive review aims to summarize the latest advancements in the therapeutic approaches for PVR, encompassing historical perspectives, current surgical techniques, pharmacological interventions, biological and genetic therapies, and novel experimental treatments. Traditional surgical methods, such as vitrectomy, have been refined with advanced instrumentation and techniques to improve outcomes. Pharmacological treatments, including anti-inflammatory and anti-proliferative agents, are being explored to prevent and manage PVR. Emerging therapies, such as stem cell and gene therapy, offer promising new avenues for treatment. Despite these advancements, challenges remain in preventing recurrence and improving long-term outcomes. This review highlights the progress made and identifies areas for future research, emphasizing the importance of continued innovation to enhance patient care and reduce the burden of PVR.

Role of Robotic-Assisted Surgery in Public Health: Its Advantages and Challenges

The modern hospital setting is closely related to engineering and technology. In a hospital, modern equipment is abundant in every department, including the operating room, intensive care unit, and laboratories. Thus, the quality of treatment provided in hospitals and technology advancements are closely tied. Robotic systems are used to support and improve the accuracy and agility of human surgeons during medical procedures. This surgical approach is commonly referred to as robotic surgery or robotic-assisted surgery (RAS). These systems are not entirely autonomous; they are managed by skilled surgeons who carry out procedures with improved accuracy and minimized invasiveness using a console and specialized instruments. Because RAS offers increased surgical precision, less discomfort after surgery, shorter hospital stays, and faster recovery time, all of which improve patient outcomes and lessen the strain on healthcare resources, it plays a critical role in public health. Its minimally invasive technique benefits patients and the healthcare system by lowering problems, reducing the requirement for blood transfusions, and reducing the danger of infections related to medical care. Furthermore, the possibility of remote surgery via robotic systems can increase access to specialized care, reducing regional differences and advancing fairness in public health. In this review article, we will be covering how RAS has its role in public health.

A Theoretical Exploration of Artificial Intelligence's Impact on Feto-Maternal Health from Conception to Delivery

The implementation of Artificial Intelligence (AI) in healthcare is enhancing diagnostic accuracy in clinical setups. The use of AI in healthcare is steadily increasing with advancing technology, extending beyond disease diagnosis to encompass roles in feto-maternal health. AI harnesses Machine Learning (ML), Natural Language Processing (NLP), Artificial Neural Networks (ANN), and computer vision to analyze data and draw conclusions. Considering maternal health, ML analyzes vast datasets to predict maternal and fetal health outcomes, while NLP interprets medical texts and patient records to assist in diagnosis and treatment decisions. ANN models identify patterns in complex feto-maternal medical data, aiding in risk assessment and intervention planning whereas, computer vision enables the analysis of medical images for early detection of feto-maternal complications. AI facilitates early pregnancy detection, genetic screening, and continuous monitoring of maternal health parameters, providing real-time alerts for deviations, while also playing a crucial role in the early detection of fetal abnormalities through enhanced ultrasound imaging, contributing to informed decision-making. This review investigates into the application of AI, particularly through predictive models, in addressing the monitoring of feto-maternal health. Additionally, it examines potential future directions and challenges associated with these applications.

Large Language Models in Oncology: Revolution or Cause for Concern?

The technological capability of artificial intelligence (AI) continues to advance with great strength. Recently, the release of large language models has taken the world by storm with concurrent excitement and concern. As a consequence of their impressive ability and versatility, their provide a potential opportunity for implementation in oncology. Areas of possible application include supporting clinical decision making, education, and contributing to cancer research. Despite the promises that these novel systems can offer, several limitations and barriers challenge their implementation. It is imperative that concerns, such as accountability, data inaccuracy, and data protection, are addressed prior to their integration in oncology. As the progression of artificial intelligence systems continues, new ethical and practical dilemmas will also be approached; thus, the evaluation of these limitations and concerns will be dynamic in nature. This review offers a comprehensive overview of the potential application of large language models in oncology, as well as concerns surrounding their implementation in cancer care.

Revitalizing neurosurgical frontiers: The EANS frontiers in neurosurgery committee's strategic framework

Introduction

The field of neurosurgery faces challenges with the increasing involvement of other medical specialties in areas traditionally led by neurosurgeons. This paper examines the implications of this development for neurosurgical practice and patient care, with a focus on specialized areas like pain management, peripheral nerve surgery, and stereotactic radiosurgery.

Research question

To assess the implications of the expanded scope of other specialties for neurosurgical practice and to consider the response of the EANS Frontiers in Neurosurgery Committee to these challenges.

Materials and methods

Analysis of recent trends in neurosurgery, including the shift in various procedures to other specialties, demographic challenges, and the emergence of minimally invasive techniques. This analysis draws on relevant literature and the initiatives of the Frontiers in Neurosurgery Committee.

Results

We explore a possible decrease in neurosurgical involvement in certain areas, which may have implications for patient care and access to specialized neurosurgical interventions. The Frontiers in Neurosurgery Committee's role in addressing these concerns is highlighted, particularly in terms of training, education, research, and networking for neurosurgeons, especially those early in their careers.

Discussion and conclusion

The potential decrease in neurosurgical involvement in certain specialties warrants attention. This paper emphasizes the importance of carefully considered responses by neurosurgical societies, such as the EANS, to ensure neurosurgeons continue to play a vital role in managing neurological diseases. Emphasis on ongoing education, integration of minimally invasive techniques, and multidisciplinary collaboration is essential for maintaining the field's competence and quality in patient care.