SAT-469 Update on the Use of Robotic Surgery and Artificial Intelligence for Adrenal Tumors

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

Since its introduction, robotic surgery has experienced rapid development and has been extensively implemented across various medical disciplines. It is crucial to comprehend the advancements in research and the evolutionary trajectory of its thematic priorities. This research conducted a bibliometric analysis on the literature pertaining to robotic surgery, spanning the period from 2014 to 2023, sourced from the Web of Science database. The objective was to delineate the publication trends and trace the development of research topics within the domain of robotic surgery. From 2014 to 2023, there has been a consistent upward trend in the annual volume of publications concerning robotic surgery. The United States emerges as the leading country in terms of both the number of publications (n=3402) and citations (n=57731). The Journal of Robotic Surgery has the highest number of publications (n=506), while IEEE Transactions on Robotics has the highest number of citations (n=53). Yonsei University is the institution with the greatest number of publications (n=196), and the University of Washington has the highest average citation count (n=30). Alexandre Mottrie is the author with the most publications and citations (n=70 publications, n=1816 citations). Keyword analysis revealed seven distinct clusters: (1) applications and techniques of robotic surgery; (2) urological surgery and associated complications; (3) gastrointestinal diseases and surgical interventions; (4) robotic thyroid surgery and related complications; (5) gynecological diseases and corresponding surgical procedures; (6) Da Vinci robot and its training; (7) pulmonary diseases and associated surgeries. Artificial intelligence (AI) has been identified as a newly emerging keyword in the field. The corpus of literature on robotic surgery has seen a steady rise over the past decade, marked by extensive collaboration among various countries, institutions, and researchers. This study has delineated the global trends, identified research hotspots, highlighted emerging topics, and outlined the foundational knowledge within the field of robotic surgery. Looking forward, the integration of AI with robotic surgery is poised to offer substantial benefits and is anticipated to become a pivotal trend and area of focus in the field's future advancement.

Integrating blockchain technology with artificial intelligence (AI) i.e., blockchain Intelligence makes an extremely powerful tool that solves many multidimensional problems in several domains. Blockchain technology has the potential to provide links to shared data, transactions, and records in a decentralized, safe, and reliable manner, including the information and decision-making capability of AI which makes machines similar as capable as humans. This study is intended to present an updated systematic review of the integration of Blockchain and AI in various application areas. We have studied and summarized more than 100 research papers to present an updated version of the review. We also discuss the future of Blockchain technologies with AI. By integrating these two technologies results increases the security, efficiency, and productivity of the applications. Past works feature a few possible advantages of integration of Blockchain and AI, yet just give a restricted hypothetical system to depict forthcoming certifiable combination instances of the two advances. We survey and orchestrate surviving exploration on the integration of AI and Blockchain are other ways around to thoroughly build up a future research plan on the fusion of the two innovations. We also proposed an agenda to develop a secure system of cyber threat intelligence information exchange by using features of blockchain and artificial intelligence. This paper mainly focusses on explaining how collaboration of blockchain and AI gives immense boost in latest domains like Cybersecurity, Healthcare, Supply Chain Management, Finance and Banking and Social Media Analytics.

This study investigates the influence of artificial intelligence (AI) integration on teachers’ professional identity and job satisfaction using a quantitative research design involving 251 respondents. Descriptive statistics showed relatively high levels of AI integration (M = 3.98, SD = 0.62) and professional identity (M = 4.12, SD = 0.58), indicating strong engagement with AI tools and a well-defined sense of professional role among teachers. Pearson correlation analysis revealed a moderately strong, statistically significant positive relationship between AI integration and professional identity (r = 0.612, p = 0.000), demonstrating that increased use of AI is associated with a strengthened professional identity. Mediation analysis further indicated that institutional factors significantly influence the relationship between AI integration and job satisfaction, with AI integration positively predicting institutional support (β = 0.54, p = 0.000) and institutional factors strongly predicting job satisfaction (β = 0.47, p = 0.000). Both a significant direct effect (β = 0.29, p = 0.001) and a strong indirect effect (β = 0.25, p = 0.000) were found, confirming partial mediation. These findings highlight that AI not only enhances teachers’ identity and satisfaction but that successful implementation relies heavily on institutional readiness and support. Overall, the results underscore the importance of adopting teacher-centered AI strategies that reinforce professional identity, reduce workload, and enhance well-being.

The article explores the transformative impact of Artificial Intelligence (AI) on
 engineering, focusing on the evolution over the last decade. AI has become a cornerstone in
 reliable engineering, influencing various aspects such as predictive maintenance, fault
 detection, optimization, automation, and decision support. Predictive maintenance, enabled by
 AI algorithms, revolutionizes traditional approaches by analysing extensive datasets to predict
 equipment failures, allowing proactive interventions and minimizing downtime. Fault detection
 and diagnostics benefit from AI's real-time monitoring and early anomaly identification,
 reducing the risk of catastrophic failures and enhancing overall system reliability. Optimization
 of complex systems is facilitated by AI's capacity to process vast amounts of data, leading to
 improved performance and minimized resource consumption. The integration of AI in
 automation and robotics reshapes manufacturing processes, emphasizing precision and
 reliability. Simulation and modelling, data analysis, and supply chain optimization are also
 discussed as vital areas where AI contributes to enhanced reliability. The article highlights the
 importance of ethical considerations and human oversight in deploying AI responsibly,
 emphasizing a collaborative synergy between AI and human expertise for continued
 advancements in engineering solutions.

The integration of artificial intelligence in robotic surgery: A narrative review

Artificial intelligence (AI) is transforming the education sector of different nations of the world. In spite of its commonly attested huge prospects, AI is yet to be integrated into Nigerian educational systems at a significant extent. This study examines the prospects and challenges of AI integration into Nigerian educational systems. The study adopts Venkatesh’s Unified Theory of Acceptance and Use of Technology, which explains how technologies get accepted and used in society. Questionnaire, and library and internet materials are used to source data. Survey design and mixed method with their allied analytical tools are employed. The results show pedagogic, technological, informative, administrative and socio-economic benefits of AI integration into Nigerian educational systems. Besides, it identifies the challenges constraining AI integration into the sector and proffers some solutions. The study concludes that the current low level of AI awareness and integration in Nigeria limits the ideal extent of AI impact on Nigerian educational systems. Stakeholders should collaborate significantly in making efforts to increase AI integration and find lasting solutions to the problems of AI integration into the educational systems.

Artificial intelligence (AI) has emerged as a promising technology that can revolutionize surgical decision-making (SDM). This comprehensive review aims to explore the current state of AI in SDM and highlight its benefits, challenges, and future directions. The integration of AI in SDM offers numerous advantages. AI algorithms can analyze medical images, such as radiographs, computed tomography scans, and magnetic resonance imaging, to detect abnormalities and assist in pre-operative assessments. By leveraging electronic health records, AI can provide personalized surgical recommendations based on patient-specific data.Additionally, AI can analyze genetic data to assess genetic predispositions and tailor treatment plans accordingly. Intra-operatively, AI can aid in real-time analysis of surgical videos and imaging, helping surgeons identify critical structures and guide precise incisions. AI algorithms can also monitor physiological indicators to detect early signs of complications and predict outcomes, improving intra-operative decision-making. Post-operatively, AI can analyze vital signs, imaging, and patient data to detect complications, provide outcomes analysis, and facilitate personalized patient care. However, challenges and limitations exist. Data quality and availability, interpretability of AI algorithms, data security, integration into surgical workflows, and regulatory considerations are important challenges. Addressing these challenges involves ensuring data privacy, developing transparent AI models, establishing robust infrastructure, engaging clinicians, and establishing regulatory frameworks. AI-powered surgical robots and systems can enhance surgical precision and automation. Improvements in interpretability and explainability foster trust and ethical considerations. Also, data sharing and collaboration advancements could refine AI algorithms' accuracy and generalizability. Personalized medicine and precision surgery are achieved through AI integration. Also, education and training could benefit from AI-powered decision support systems.

Background: Artificial intelligence (AI) is increasingly embedded in routine primary care, yet how the levels of integration might affect its acceptability is unknown, especially in relationship-based service models where patients expect visible human stewardship. Prior experimental studies often treat AI adoption as a binary condition, leaving the "intensity gradient" of automation and the role of model specialization under-explored. We examine whether increasing AI integration in the clinical encounter erodes patients' intention to seek care from family doctors in China, and whether labeling the AI as a medical-specific model buffers such erosion. Methods: We conducted a nationwide online survey experiment in China (N = 2790). Participants were randomly assigned to vignettes that varied by (i) the level of AI integration (low, medium, high) and (ii) the AI type (general-purpose vs. medical-specific large language model), with a human-only care scenario as a reference. Care-seeking intention from family doctors was assessed immediately after exposure. We estimated treatment effects using OLS regression with heteroskedasticity-robust standard errors, and examined the buffering hypothesis through an interaction term between AI integration intensity and AI type. Results: Care-seeking intention declined steadily as AI integration increased (p < 0.001), with the sharpest drop under high-intensity AI integration where clinical decisions were delegated to the AI system. Across all intensity levels, framing the system as a medical-specific AI consistently resulted in higher care-seeking intention than a general-purpose model. However, the interaction between AI intensity and the AI type was not statistically significant (p = 0.508). Conclusions: Patient acceptance of AI in primary care depends not only on whether AI is involved, but on how deeply AI is positioned in the encounter. Medical-specific AI labeling may enhance acceptance across all AI integration levels. The findings underscore the need to preserve human clinical agency in AI-embedded primary care. The results contribute to research on healthcare systems, digital health, and AI-patient interaction.

This in-depth research explores the emerging relationship between artificial intelligence (AI) and legal systems by addressing key questions and understanding the evolution of global justice systems. This study focuses on the role of AI in strengthening the efficiency and objectivity of the judiciary, especially through the application of AI as judges in countries such as China and Estonia. This research aims to systematically analyse these developments, examining how AI is being integrated into justice systems in different parts of the world with challenges related to ethics, accountability, and human rights. The study results show that the integration of AI in the legal system brings increased efficiency and potential for transparency but also raises serious concerns about bias in AI algorithms, limitations in interpreting complex laws, and the impact on human rights principles. The main findings of this research show that the integration of AI in the legal system contains great potential for transformation but also requires a careful approach. While AI can improve the efficiency and quality of decision-making, it is important that AI is developed and implemented within a solid legal and ethical framework that respects human rights and maintains the justice system's integrity. This research emphasizes the need to consider each country's unique legal, cultural, and social context when adopting AI into their legal systems.

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

The rise of artificial intelligence (AI) heralds a significant revolution in healthcare, particularly in mental health.AI's potential spans diagnostic algorithms, data analysis from diverse sources, and real-time patient monitoring. It is essential for clinicians to remain informed about AI's progress and limitations. The inherent complexity of mental disorders, limited objective data, and retrospective studies pose challenges to the application of AI. Privacy concerns, bias, and the risk of AI replacing human care also loom. Regulatory oversight and physician involvement are needed for equitable AI implementation. AI integration and use in psychotherapy and other services are on the horizon. Patient trust, feasibility, clinical efficacy, and clinician acceptance are prerequisites. In the future, governing bodies must decide on AI ownership, governance, and integration approaches. While AI can enhance clinical decision-making and efficiency, it might also exacerbate moral dilemmas, autonomy loss, and issues regarding the scope of practice. Striking a balance between AI's strengths and limitations involves utilizing AI as a validated clinical supplement under medical supervision, necessitating active clinician involvement in AI research, ethics, and regulation. AI's trajectory must align with optimizing mental health treatment and upholding compassionate care.

Academic Summary Firms face intensifying pressure to innovate amid escalating US–China trade tensions. This study investigates how they integrate artificial intelligence (AI) to respond to such disruptions. We reconceptualize induced innovation theory as adversity‐induced innovation theory and test hypotheses using data from listed Chinese manufacturing firms, WTO tariff records, and customs data (2015–2022). Results indicate that higher tariff exposure increases AI integration, particularly among firms with stronger customer engagement needs. AI integration improves supply chain efficiency, sales performance, and market value. However, performance benefits follow an inverted U‐shape: extreme tariff exposure diminishes AI's compensatory effects. The study extends induced innovation theory to geopolitical adversities and advances stakeholder engagement theory by identifying customer engagement as a boundary condition for technology adoption. We establish causal relationships between tariff‐induced AI integration and firm performance through instrumental variable analysis and robustness checks. Findings inform how firms can strategically use technological innovation and customer collaboration to strengthen resilience under external shocks. Managers should view trade tensions as catalysts for AI integration rather than merely as obstacles. Managerial Summary Firms facing trade tensions should view them as catalysts for strategic AI integration rather than merely as obstacles. Our research shows that firms experiencing higher tariff exposure that invest in AI achieve measurable improvements in supply chain efficiency, sales performance, and market valuations. However, AI's compensatory benefits diminish at extreme tariff levels, revealing an optimal investment threshold that managers should consider. The benefits of AI integration are particularly strong for firms with high customer engagement needs, such as those with concentrated customer bases, substantial international sales, or significant customer‐specific investments. To maximize returns, managers should prioritize AI technologies that strengthen customer relationships by enhancing communication, responsiveness, and service personalization during trade disruptions. Successful implementation requires navigating technical and organizational challenges, including talent acquisition, data quality concerns, and integration complexities. Despite these hurdles, our findings confirm AI's strategic value as a compensatory innovation tool that not only mitigates tariff impacts but also transforms supplier‐customer relationships. By aligning AI initiatives with customer engagement objectives, firms can turn geopolitical adversity into a competitive advantage. Companies that integrate AI strategically can maintain operational continuity, preserve market position, and emerge stronger from trade disruptions.

The integration of data science and Artificial Intelligence (AI) into geospatial analysis has revolutionized Earth observation, driving progress towards the Sustainable Development Goals (SDGs). Recent developments in data acquisition technologies like high-resolution satellites and sensors have generated vast and diverse datasets for monitoring environmental changes and managing natural resources. Concurrently, innovations in Machine Learning (ML) and AI have significantly enhanced the processing, analysis and interpretation of this geospatial data. Techniques such as deep learning, spatial data mining and automated feature extraction are now essential to deriving actionable insights from complex geospatial datasets. This paper reviews the latest trends and breakthroughs in the application of AI/ML to geospatial data for Earth observation, emphasizing their role in advancing the SDGs. Key areas of focus include improved algorithms for land cover classification, disaster prediction and climate monitoring. These technologies enable more precise and timely responses to environmental challenges, such as deforestation, urbanization and natural disasters, thereby supporting sustainable management and policymaking. Furthermore, the integration of AI with geospatial data enhances predictive modelling, scenario planning and decision support systems, which are critical for achieving SDG targets related to environmental sustainability and resilience. The synthesis of recent research and technological developments highlights the potential of AI/ML approaches for geospatial analysis and their alignment with global sustainability goals. The outcomes underline the requirement for continued innovation and collaboration across disciplines to fully leverage these advancements for effective Earth observation and sustainable development.

Background. In the context of the continuous shifts in the modern labour market and the escalating competitiveness among students of maritime specialties, there is an increasing demand for the training of highly qualified specialists in the maritime industry, who are able to interact effectively with international partners at a professional level. In this respect, the use of generative artificial intelligence (AI) to improve foreign language teaching methods for students of maritime subjects is especially relevant. This is fully justified under the conditions of globalisation and the digital transformation of the educational sphere. Purpose. The purpose is to determine the effectiveness of generative AI application in the process of building foreign language professional communicative competence of maritime students by modelling realistic scenarios of job interviews in shipping companies. Materials and methods. The present study adopted a comprehensive approach, integrating theoretical and empirical methods of analysis. The theoretical basis was established through a comprehensive review of normative and educational documents, with the aim of identifying the fundamental requirements for the educational process. This was complemented by a thorough examination of scientific and educational literature, as well as methodological literature, on the application of AI in education and linguodidactics, thus enabling the identification of current trends and the potential of technologies for teaching maritime students. The empirical element of the study involved modelling, describing and analysing professional interview scenarios using generative AI models, the observation of students' learning activities, and the conduction of interviews to obtain feedback on the perception and effectiveness of the new techniques. Results. The research yielded an innovative methodology for training students of maritime specialities at Sevastopol State University for professional interviews in international shipping companies. The developed three-stage training model, combining the creation of a database of questions, simulation of interviews using generative (AI) and practising skills through business games, demonstrated high efficiency in the formation of both linguistic and professional competences of students, in addition to soft skills deemed integral for a successful career in the maritime industry. The integration of generative AI within the training framework of maritime students has been found to expand the scope of opportunities for effective preparation for professional interviewing. The implementation of this methodology has been identified as a significant contributor to the modernisation of the educational process, rendering it more personalised, effective and aligned with the precise requirements of the contemporary labour market.