Role of Artificial Intelligence in Surgical Decision-Making: A Comprehensive Review.

Artificial Intelligence (AI) is revolutionizing the accounting profession, offering transformative capabilities for automating tasks, enhancing decision-making, and improving financial accuracy. As AI becomes integral to accounting practices, it brings both significant opportunities and notable ethical challenges. This review examines the intersection of AI and ethical accounting, providing insights into how professionals can navigate the evolving landscape. The adoption of AI in accounting introduces opportunities for increased efficiency and accuracy. AI systems can handle repetitive tasks such as data entry, reconciliation, and transaction categorization, freeing accountants to focus on strategic activities. Advanced AI algorithms can analyze large volumes of financial data to identify patterns, detect anomalies, and provide real-time insights, enhancing decision-making and forecasting accuracy. Moreover, AI-driven predictive analytics can aid in risk assessment and management, helping organizations to anticipate and mitigate potential financial threats. However, the integration of AI in accounting also raises significant ethical concerns. One of the primary challenges is ensuring transparency and accountability in AI decision-making processes. As AI systems often operate as "black boxes," understanding and explaining their outputs can be difficult, potentially leading to issues of trust and compliance. Ethical accounting necessitates that AI systems be designed with transparency in mind, providing clear explanations for their decisions and actions. Data privacy and security represent another critical ethical consideration. The extensive use of financial data by AI systems necessitates robust measures to protect sensitive information from breaches and unauthorized access. Accountants must ensure that AI systems comply with data protection regulations and ethical standards, safeguarding the confidentiality and integrity of financial data. Bias and fairness in AI algorithms are also pressing ethical issues. If not properly addressed, biases in AI systems can lead to unfair outcomes, such as biased financial recommendations or discriminatory practices. Ensuring fairness requires ongoing monitoring and evaluation of AI systems to identify and mitigate biases. In conclusion, while AI offers substantial benefits for the accounting profession, it also presents ethical challenges that must be carefully managed. Accountants must navigate these challenges by promoting transparency, ensuring data privacy and security, and addressing biases in AI systems. By doing so, the accounting profession can harness the potential of AI while upholding ethical standards and maintaining public trust. Keywords: AI, Ethical Accounting, Navigating, Challenges, Opportunities.

cancer remains a leading cause of morbidity and mortality worldwide, with nearly 20 million new cases and 9.7 million deaths reported in 2022. The increasing burden of cancer, driven by population growth and aging, necessitates innovative solutions to improve diagnosis, prognosis, and treatment outcomes. Artificial Intelligence has emerged as a transformative tool in oncology, offering significant potential in cancer detection, diagnosis, and personalized treatment strategies. This review explores the real-world applications of Artificial Intelligence in oncology, focusing on lung cancer and breast cancer, two of the most prevalent and deadly cancers globally. Artificial Intelligence-driven technologies, particularly in imaging, pathology, and genomics, have demonstrated remarkable success in enhancing early detection, diagnostic accuracy, and treatment planning. In lung cancer, Artificial Intelligence-powered imaging tools, such as deep learning models, have shown high sensitivity and specificity in detecting small pulmonary nodules, often missed by traditional methods. Similarly, in breast cancer, Artificial Intelligence has proven effective in mammography interpretation, reducing false positives and false negatives, and alleviating the workload of radiologists. Despite its promising potential, the integration of Artificial Intelligence into clinical practice faces several challenges, including issues related to data quality, algorithmic biases, and ethical considerations. The "black box" nature of many Artificial Intelligence systems poses a significant barrier to clinical acceptance, highlighting the need for explainable Artificial Intelligence to provide transparent and interpretable decision-making processes. Furthermore, the successful implementation of Artificial Intelligence in oncology requires robust regulatory frameworks and standardized protocols to ensure patient safety and data security. This review underscores the transformative potential of Artificial Intelligence in revolutionizing cancer care, emphasizing the importance of addressing key challenges to harness its full potential. By enhancing early detection, reducing diagnostic errors, and enabling personalized treatment strategies, Artificial Intelligence has the potential to significantly improve patient outcomes and reduce the global burden of cancer. However, its successful integration into clinical practice will depend on interdisciplinary collaboration, ethical considerations, and a commitment to responsible implementation.

In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalized medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted. We provide a brief review of AI in image- guided biopsy of bone tumours (primary and secondary) and specimen handling, in the era of personalized medicine. This article explores AI's role in enhancing diagnostic accuracy, improving safety in biopsies, and enabling more precise targeting in bone lesion biopsies, ultimately contributing to better patient outcomes in personalized medicine. We dive into various AI technologies applied to osseous biopsies, such as traditional machine learning, deep learning, radiomics, simulation, and generative models. We explore their roles in tumour-board meetings, communication between clinicians, radiologists, and pathologists. Additionally, we inspect ethical considerations associated with the integration of AI in bone biopsy procedures, technical limitations, and we delve into health equity, generalizability, deployment issues, and reimbursement challenges in AI-powered healthcare. Finally, we explore potential future developments and offer a list of open-source AI tools and algorithms relevant to bone biopsies, which we include to encourage further discussion and research.

Purpose This study aims to investigate ethical consideration into the data quality management and biases for fairness in artificial intelligence (AI) algorithms. It looks at how biases in AI systems could make it difficult to maintain data quality control in medical travel and tourism. Design/methodology/approach The systematic review of the literature from January 2019 to March 2025 (72 out of 925 articles) considers a variety of keyword combinations used for data quality control in AI systems. The process of systematic literature review aids in identifying studies on data quality management for medical tourism and travel. Findings The results of the study show that data quality visibility should be increased, data rectification should be done consistently and data momentum should be managed. It indicates that audience behavior in medical travel and tourism is significantly impacted by AI-powered digital branding and marketing. Research limitations/implications The research is limited to the single source of methodology, and it is limited in terms of articles collected from Jan 2019 to March 2025. It can be extended to the last decade for more insights and issues in AI use for medical travel and tourism. Practical implications Practical applications include efficient patient processing for international medical travel and tourism, as well as automated AI machine learning. Future research investigations should consistently segment data evaluation to overcome the limited regulatory compliance of data quality management. Social implications Patients (medical tourism) are primary elements in healthy social settings that can be facilitated through AI’s efficient use and maintaining the quality of the data management. Originality/value This study aims to address the main ethical concerns about the quality of AI data, data use monitoring and ethical support for data decisions and implementations inside the AI framework. The usage of digital branding, mobile app development, digital audience behavior, digital marketing principles and artificial intelligence in marketing are some of the important concepts used to accomplish the research goals.

The integration of Artificial Intelligence (AI) in healthcare is revolutionizing diagnostics, offering important opportunities while emerging challenges. This essay explores the potential and obstacles of AI-based diagnostics. Key problems include technological and ethical issues such as data privacy, compatibility of devices , and regulatory issues. Ethical considerations also arise around patient privacy, accountability for AI errors, legal frameworks, and algorithm transparency. Despite these challenges, AI showcases substantial opportunities for the improvement of diagnostics and treatment techniques. Its capabilities include earlier disease detection, personalized treatment pathways, and healthcare professional support, particularly in radiology, pathology, and genomics. Additionally, AI can help and reduce the cost of healthcare access by streamlining resources, optimizing staff deployment, and facilitating remote monitoring and telemedicine, especially for developing countries. With the analysis of data from wearable devices and enabling remote consultations, AI helps us in the proactive management of chronic conditions. AI-based diagnostics can help overcome long term healthcare challenges, enhancing precision and treatment efficiency. A balanced approach is essential, addressing ethical, privacy, and legal considerations while emphasizing AI’s role as a support for healthcare professionals. Future research should focus on AI algorithm transparency, ethics, and the expansion of telemedicine, especially in underserved areas.

Endoscopy remains the gold standard for gastrointestinal (GI) diagnostics, enabling direct visualization and intervention within the GI tract. However, diagnostic accuracy and procedural outcomes vary significantly depending on the endoscopist's skill and experience, leading to potential missed lesions and inconsistent patient care. The integration of artificial intelligence (AI) into endoscopic practice offers a promising solution to address these limitations and enhance diagnostic precision. This review explores the current applications of AI in endoscopy, focusing on image analysis, lesion detection, classification, and workflow optimization, while evaluating the impact on clinical practice and identifying implementation challenges. A literature search was conducted using PubMed, Google Scholar, and IEEE Xplore databases for studies published between January 2010 and December 2024. Keywords included "Artificial Intelligence," "Endoscopy," "Gastrointestinal Diseases," "Image Analysis," and "Lesion Detection." Studies were selected based on their focus on AI applications in endoscopy with quantitative or qualitative data on performance and clinical impact. AI demonstrates exceptional capabilities in polyp detection, achieving detection rates that often surpass those of human practitioners, with systems such as GI Genius showing high sensitivity and specificity. Convolutional neural networks excel in real-time lesion identification and classification, differentiating between benign and malignant growths with remarkable precision. AI also optimizes endoscopic workflows through automated reporting and advanced training tools. Although AI integration shows promise for enhancing endoscopic diagnostic accuracy and procedural efficiency, successful implementation requires careful consideration of current limitations, including reliance on industry-sponsored studies, and addressing challenges in data quality, clinical workflow integration, and regulatory considerations. Future developments in advanced algorithms, personalized medicine, and telemedicine may further advance endoscopic practice and improve patient outcomes.

The integration of artificial intelligence (AI) into various fields has ushered in a new era of multidisciplinary progress. Defined as the ability of a system to interpret external data, learn from it, and adapt to specific tasks, AI is poised to revolutionize the world. In forensic medicine and pathology, algorithms play a crucial role in data analysis, pattern recognition, anomaly identification, and decision making. This review explores the diverse applications of AI in forensic medicine, encompassing fields such as forensic identification, ballistics, traumatic injuries, postmortem interval estimation, forensic toxicology, and more. A thorough review of 113 articles revealed a subset of 32 papers directly relevant to the research, covering a wide range of applications. These included forensic identification, ballistics and additional factors of shooting, traumatic injuries, post-mortem interval estimation, forensic toxicology, sexual assaults/rape, crime scene reconstruction, virtual autopsy, and medical act quality evaluation. The studies demonstrated the feasibility and advantages of employing AI technology in various facets of forensic medicine and pathology. The integration of AI in forensic medicine and pathology offers promising prospects for improving accuracy and efficiency in medico-legal practices. From forensic identification to post-mortem interval estimation, AI algorithms have shown the potential to reduce human subjectivity, mitigate errors, and provide cost-effective solutions. While challenges surrounding ethical considerations, data security, and algorithmic correctness persist, continued research and technological advancements hold the key to realizing the full potential of AI in forensic applications. As the field of AI continues to evolve, it is poised to play an increasingly pivotal role in the future of forensic medicine and pathology.

Educational technologies leverage artificial intelligence and adaptive learning algorithms to analyze learners' performance and provide personalized feedback and recommendations. This helps identify areas of improvement and tailor learning experiences accordingly. Adaptive learning and artificial intelligence (AI) have emerged as powerful tools in the field of education, revolutionizing traditional teaching methods. This article explores the integration of adaptive learning technologies with AI algorithms to personalize and enhance the learning experience for students. The use of AI allows for the collection and analysis of vast amounts of data, enabling the system to adapt and tailor the content and delivery of educational materials to meet the individual needs of each student. By continuously monitoring and evaluating student performance, AI algorithms can identify areas of weakness and provide targeted interventions to address them. This personalized approach not only improves learning outcomes but also promotes student engagement and motivation. Additionally, AI-powered adaptive learning systems have the potential to assist teachers by automating administrative tasks, providing real-time feedback, and generating comprehensive progress reports. However, challenges such as privacy concerns, ethical considerations, and the need for teacher training in utilizing these technologies must be addressed. Overall, the integration of adaptive learning and AI holds great promise in transforming education by creating personalized and effective learning experiences for students.

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

This research explores the integration and impact of artificial intelligence (AI) within Emirati media organizations, focusing on adoption determinants, obstacles, and interventions. Utilizing a mixed-methods approach, the study incorporates qualitative data from 21 in-depth interviews with media professionals and a thematic analysis of 30 scholarly articles published between 2019 and 2024. The research aims to provide a nuanced understanding of AI's transformative effects on the UAE media landscape. Key findings reveal that AI is significantly enhancing content creation, distribution, and audience engagement processes within Emirati media organizations. AI technologies such as machine learning, natural language processing, and predictive analytics are being leveraged to improve operational efficiency, streamline workflows, and deliver personalized content to audiences. These advancements are driving competitive advantages and elevating the overall quality of media output. Despite these benefits, several challenges hinder widespread AI adoption. Technological barriers, including limited access to advanced AI tools and infrastructure, pose significant obstacles. Additionally, there is a pronounced shortage of skilled personnel capable of implementing and managing AI systems, which exacerbates concerns over job displacement and workforce anxiety. Ethical considerations, such as ensuring unbiased AI algorithms and protecting user privacy, are also critical issues that require attention. The study underscores the importance of continuous training and upskilling for media professionals to adapt to the evolving technological landscape. It highlights the role of robust ethical guidelines and governance frameworks in promoting responsible AI use. Government support, through targeted policies, funding initiatives, and incentives, is identified as a crucial factor in facilitating AI integration and addressing adoption barriers. The research concludes by providing strategic recommendations for media organizations and policymakers. It advocates for the development of comprehensive AI adoption roadmaps that include capacity-building initiatives, ethical oversight, and collaborative efforts between industry stakeholders. By addressing these areas, Emirati media organizations can harness the full potential of AI to drive innovation, improve audience engagement, and maintain competitiveness in a rapidly digitizing global media environment. This study contributes to the growing body of knowledge on AI in media, offering valuable insights into the specific context of the UAE. It provides a foundation for future research on AI's role in media and serves as a guide for organizations seeking to navigate the complexities of AI adoption.

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Artificial Intelligence (AI) has emerged as one of the most transformative technological developments of the modern era, offering a profound impact on various sectors, including education. The integration of AI into education presents a complex interplay of opportunities, challenges, and risks that require comprehensive exploration. This study adopts a mixed-methods approach, utilizing bibliometric, conceptual, and hermeneutic analyses to examine the role of AI in educational contexts. Bibliometric analysis quantitatively and visually maps research trends, employing tools to analyze 8,288 documents retrieved from the Scopus database using the keywords "Artificial Intelligence" OR "AI" AND "Education." Over the past two decades, the field has seen exponential growth, with contributions from 28,740 authors worldwide. The mapping highlights key areas of AI application, such as machine learning, deep learning, educational engineering, and computer-aided instruction, alongside technologies like ChatGPT, virtual reality, big data, and data mining. Conceptual analysis dissects critical ideas such as posthumanism, transhumanism, and their intersection with AI in education. These paradigms are reshaping education by challenging its traditional roles as a vehicle for human development and societal progress. AI's role in facilitating personalized learning experiences underscores its potential for enhancing efficiency and adaptability. However, it also raises pressing questions about its alignment with broader educational objectives. Beyond knowledge transfer, education fosters character development, critical thinking, and social skills areas potentially undermined by overreliance on AI technologies. The hermeneutic method provides a deeper interpretation of philosophical texts and literature, emphasizing the ethical and societal implications of integrating AI into education. Posthumanism and transhumanism, which advocate for merging human capabilities with technological advancements, pose existential challenges to education's foundational values. Tools like ChatGPT, while powerful, risk eroding academic integrity and critical skill development. For instance, reliance on AI for solving exam problems or answering complex queries undermines the principles of effort and intellectual growth, essential components of education. The research underscores the dual-edged nature of AI in education. On one hand, it enables adaptive and efficient learning systems. On the other hand, it disrupts traditional educational practices, raising ethical dilemmas and questions about the future of human-centric learning. Philosophical perspectives are crucial in navigating these disruptions, advocating for an education system that balances technological integration with the preservation of core academic values. This involves addressing the ethical considerations surrounding AI applications, particularly the potential misuse of tools like ChatGPT, which are byproducts of posthumanist and transhumanist ideologies. Education, as a transformative process, must evolve to integrate AI responsibly, ensuring that it complements rather than replaces human intellectual efforts. This requires reconstructing educational systems to mitigate the unintended consequences of AI technologies. Philosophical inquiry into the purpose of education must guide the realignment of its goals, emphasizing how knowledge is acquired and skills are nurtured in a rapidly evolving digital landscape. By fostering awareness of the ethical and practical implications of AI, educational institutions can reclaim their role in cultivating well-rounded individuals capable of critical thought and ethical decision-making. In conclusion, this study highlights the need for a balanced approach to integrating AI into education. While technological advancements offer unprecedented opportunities for innovation, they must not overshadow the humanistic values at the core of education. By addressing the challenges posed by posthumanism and transhumanism, the educational sector can harness AI's potential while safeguarding its mission to develop responsible, ethical, and capable individuals for the 21st century.

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.

Artificial intelligence (AI) has emerged as a transformative technology with the potential to revolutionize healthcare delivery, from diagnosis and treatment to patient care and administrative tasks. the applications, challenges, and ethical considerations surrounding the role of AI in healthcare. It discusses how AI algorithms can analyze vast amounts of medical data to assist healthcare professionals in making accurate diagnoses, predicting patient outcomes, and personalizing treatment plans. Additionally, it examines the challenges associated with implementing AI in healthcare, such as data privacy concerns, algorithm bias, and regulatory hurdles. Furthermore, it addresses ethical considerations, including transparency, accountability, and the impact of AI on patient-provider relationships. Despite these challenges, AI holds tremendous promise for improving healthcare efficiency, accessibility, and quality, provided that stakeholders address these concerns and harness AI's potential responsibly.

The fusion of wireless networks, the Internet of Things (IoT), and artificial intelligence (AI) holds transformative potential, ushering in an era where ubiquitous connectivity and intelligent decision-making reshape our world. This paper delves into the profound implications of this convergence, examining the interactions between wireless networks, IoT devices, and AI algorithms. Through an exploration of the evolution of wireless technologies, including the advent of 5G and beyond, the paper highlights the foundation upon which this convergence is built. The synergy of IoT, encompassing sensor-laden objects and data-driven insights, intertwines seamlessly with AI, empowering machines to emulate human cognition. The intricate relationships and dynamics between these paradigms unveil novel applications and unprecedented opportunities. From smart cities optimizing urban living to healthcare systems providing remote patient monitoring, the integration of wireless networks, IoT, and AI is witnessed across diverse domains. This paper navigates through real-world use cases that showcase how this convergence enhances efficiency, accuracy, and resource management. However, with these benefits come challenges. Data privacy, security, and ethical considerations are magnified in this interconnected landscape. Scalability and interoperability pose technical hurdles, necessitating collaborative efforts to ensure responsible and sustainable development. As this convergence advances, emerging trends like edge computing, AI-enabled autonomy, and quantum computing chart the course for the future. These innovations herald a world where seamless connectivity, intelligent decision-making, and exponential computation reshape our understanding of what's possible. In conclusion, the intersection of wireless networks, IoT, and AI transcends mere technological integration. It represents a paradigm shift that bridges the virtual and physical worlds, fostering a connected ecosystem with immense promise and a host of responsibilities. Through exploration, innovation, and ethical considerations, we are poised to harness the full potential of this convergence, shaping a future that is both technologically advanced and ethically aligned.