Artificial intelligence in HPB surgery: a narrative review on technological advances and imperatives for ethical integration.

A few weeks ago, we published an editorial discussion on whether artificial intelligence applications should be authors of academic articles [1] . We were delighted to receive more than one interesting reply letter to this editorial in a short time [2, 3] . We hope that opinions on this

“Modern Commerce – Trends and Practice” offers a comprehensive exploration of the evolving landscape of commerce, providing readers with valuable insights into the latest trends, technological advancements, and the impact of digital transformation on traditional business models. The book is structured into twelve informative chapters, each focusing on a specific aspect of modern commerce, making it an essential resource for business professionals, entrepreneurs, and students alike. The first chapter takes readers through the historical evolution of commerce, tracing its journey from traditional methods to the digital age. It explores the technological breakthroughs that have revolutionized how business is conducted, highlighting key milestones and the growing influence of globalizastion on commerce. It also delves into the regulatory changes that have shaped the industry and the emergence of new markets and trends that are defining the future of commerce. As the digital era has reshaped retail, Chapter 2 examines the profound impact of e-commerce on traditional retail, addressing consumer behavior shifts and the technology adoption that has become essential for staying competitive. It also provides case studies of successful e-commerce strategies and discusses the integration of online and offline channels to create a seamless customer experience. Mobile commerce is the focal point of Chapter 3, where the book explores the rise of mobile shopping, mobile payment systems, and the growing importance of mobile apps and websites. The chapter discusses consumer preferences and concerns related to security and privacy while highlighting successful mobile commerce platforms. Data analytics takes center stage in Chapter 4, with a detailed introduction to the various types of analytics—descriptive, predictive, and prescriptive—and how businesses can leverage these insights for better decision-making. It addresses the challenges and ethical considerations associated with data use while showcasing case studies of data analytics applications in modern commerce. Social media’s transformative effect on commerce is explored in Chapter 5, covering social media marketing strategies, influencer marketing, and the role of user-generated content. The chapter also looks at measuring social media return on investment (ROI) and how businesses can handle social media crises. Chapter 6 explores the concept of omnichannel retailing, focusing on how businesses are integrating online and offline channels to enhance the customer journey. The chapter outlines the challenges in implementing omnichannel strategies and how businesses can measure success in this area. The growing role of artificial intelligence (AI) in commerce is discussed in Chapter 7, covering AI applications in e-commerce, from chatbots to AI-driven personalization. The chapter also examines the ethical and privacy considerations surrounding AI and its future impact on the industry. Supply chain management and logistics innovations are highlighted in Chapter 8, where the book explores the role of technology in transforming supply chains, including innovations in logistics, real-time tracking, and inventory management. The chapter also addresses the challenges posed by globalization. The evolution of payment systems and financial technologies is the subject of Chapter 9, which covers traditional vs. modern payment methods, the rise of FinTech, and blockchain technology. The chapter discusses security and fraud prevention and examines the regulatory environment surrounding payment technologies. In Chapter 10, customer experience and relationship management take center stage, emphasizing strategies for improving customer satisfaction, using CRM systems, and personalizing services to meet customer expectations. The chapter also highlights case studies of businesses excelling in customer service. The legal and ethical considerations of modern commerce are explored in Chapter 11, with a focus on data privacy laws, intellectual property rights, and ethical marketing practices. The chapter also examines the risks businesses face and how they can manage compliance and legal challenges. Finally, Chapter 12 looks ahead to the future of commerce, exploring emerging trends, technological innovations, and sustainability practices. The chapter offers predictions for future market dynamics, making it an essential read for those looking to stay ahead in the ever-changing world of modern commerce. “Modern Commerce – Trends and Practice” serves as a timely and authoritative guide to understanding the forces shaping contemporary business, offering practical insights and case studies that illustrate how businesses can thrive in a digital-first economy.

This year has seen an unprecedented worldwide pandemic that has been brought on by the rise of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which results in COVID-19 (coronavirus 2019) infections. COVID-19 has impacted every aspect of our lives and has required the world to rapidly mobilize to address all aspects of diagnosis and treatment of this disease. COVID-19 has brought to light the challenges of managing a completely novel infectious disease with existing diagnostics and therapeutics that were insufficient to stem the spread of COVID-19. Thus, the resources allotted toward research and development and the global cooperation of governments, scientists, and clinicians to address COVID-19 required a pace of innovation in healthcare that has never before been observed in order to address this new disease. As a result of this effort, innovations in technology to better understand, detect, and treat COVID-19 continue to be reported every day. Here at SLAS Technology, we felt it was important to highlight these advances in technology that have been made to better address all aspects of COVID-19 detection and treatment. We present here a special issue that reports how technology has been used to address COVID-19. The spread of COVID-19 across the world has shown that any hope for effective control of COVID-19 infection in the community requires the development of rapid and accurate methods for detecting COVID-19 infections. Applying existing and emerging viral detection technologies toward better COVID-19 diagnostics has resulted in incredible advances in pathogen detection innovations. Miniaturization assays that allowed for the accurate analysis and detection of SARS-CoV-2 viral nucleic acid detection or host antibody response to COVID-19 have proven to be critical.1Zhu N. Wong P.K. Advances in Viral Diagnostic Technologies for Combating COVID-19 and Future Pandemics.SLAS Technol. 2020; 25: 513-521Google Scholar, 2Tan A.S. Nerurkar S.N. Tan W.C.C. et al.The Virological, Immunological, and Imaging Approaches for COVID-19 Diagnosis and Research.SLAS Technol. 2020; 25: 522-544Google Scholar, 3Karp D.G. Cuda D. Tandel D. et al.Sensitive and Specific Detection of SARS-CoV-2 Antibodies Using a High-Throughput, Fully Automated Liquid-Handling Robotic System.SLAS Technol. 2020; 25: 545-552Google Scholar While diagnostics initially required clinical laboratory tests, these technological advances have proven critical for field testing in the community or in less well-equipped remote diagnostic testing sites. In addition to advances in detecting COVID-19 infections, leveraging technology to better understand COVID-19 disease progression and immune response is critical to developing better therapies to combat this pandemic. As a result, the molecular mechanisms of COVID-19 infection, as well as an understanding of the critical immune responses and overall biological responses to COVID-19, have been uncovered in an amazingly short amount of time. Much of this has been a result of the use of critical technologies such as single-cell analysis technologies and advances in mass cytometry.2Tan A.S. Nerurkar S.N. Tan W.C.C. et al.The Virological, Immunological, and Imaging Approaches for COVID-19 Diagnosis and Research.SLAS Technol. 2020; 25: 522-544Google Scholar The last few years have seen a paradigm shift in the development and application of artificial intelligence (AI). This has been particularly true for life sciences and biomedical applications. In order to better understand and address COVID-19, AI has played a huge role in improving detection and therapeutic drug development. Of particular importance has been the development of multiple AI-based approaches toward improving COVID-19 detection through standard chest x-ray images.4Sekeroglu B. Ozsahin I. Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks.SLAS Technol. 2020; 25: 553-565Google Scholar,5Echtioui A. Zouch W. Ghorbel M. et al.Detection Methods of COVID-19.SLAS Technol. 2020; 25: 566-572Google Scholar Applying AI toward COVID-19 diagnostics through existing standard medical imaging allows for more rapid diagnosis through telemedicine and automated tools. As AI begins to pervade every aspect of medicine, it is inevitable that advances in AI technology will be important to overcoming this pandemic. It is now clear that COVID-19 is a unique infection that affects a wide range of biological systems. One of the most affected systems has been pulmonary function. The ability to treat COVID-19 patients has often required the use of ventilators, and the lack of sufficient ventilators has been linked to poorer outcomes. The paucity of ventilators available in comparison to COVID-19 infection rates led to a number of advances in ventilator technology to increase their production speed and portability while lowering their cost.6Fang Z. Li A.I. Wang H. et al.Ambubox: A Fast-Deployable Low-Cost Ventilator for COVID-19 Emergent Care.SLAS Technol. 2020; 25: 573-584Google Scholar These advances allow patients additional time to fight off infection as well as allow emerging therapies to work. This pandemic has adversely affected the lives of so many people in so many ways. But, it has also shown that when the global community comes together to collectively address a singular problem, amazing innovations in technology can happen that provide hope for a better future after the pandemic.

Artificial intelligence (AI) offers new opportunities to enhance surgical training through automated performance assessment, adaptive learning platforms, and AI-enabled virtual or augmented reality (VR/AR) simulation. Although global literature is expanding, the UK context differs in governance, procurement, and training structures. National initiatives, such as the Royal College of Surgeons of England (RCS) Future of Surgery (FOS) programme, have highlighted AI and extended reality as priorities for modernising surgical education. However, UK peer-reviewed evidence remains limited, with most work consisting of pilot studies and early feasibility assessments. This narrative review synthesises UK-specific applications of AI in surgical training, identifies current gaps, and proposes priorities for future research.Two independent reviewers conducted a focused search of peer-reviewed and grey literature, including RCS policy documents, to identify UK-based uses of AI in surgical training. Databases searched included PubMed, the Excerpta Medica database (Embase), Scopus, and Web of Science, supplemented by targeted screening of UK policy sources. Studies were included if they involved AI or AI-enabled technologies applied to surgical education, simulation, or assessment within the UK. Non-UK studies and articles focused solely on clinical (non-educational) AI applications were excluded. Data were synthesised on AI modality, educational outcomes, feasibility, and barriers. A Risk Of Bias In Non-randomized Studies - of Interventions (ROBINS-I)-aligned risk-of-bias assessment was performed.Results: UK literature comprises national policy reports and a small number of empirical pilot studies exploring AI-enhanced VR/AR simulation, AI-driven performance analytics, and early AI components within robotic training curricula. RCS policy documents consistently identify AI as a key element of future training reform. Empirical studies report feasibility, trainee acceptability, and construct validity but provide limited evidence of improvements in operative performance or patient outcomes. Most work is single-centre and exploratory, and significant barriers, including cost, faculty training, data-governance requirements, and variability in access across deaneries, remain.Discussion: The UK is at an early yet promising stage of adopting AI within surgical education. National policy momentum and the expansion of robotic programmes provide opportunities for coordinated integration. Collaboration between NHS education bodies, simulation centres, and technology developers could support standardised metrics and equitable access. However, robust multi-centre evaluation frameworks are required to determine educational effectiveness. Ethical considerations, including data privacy, algorithmic transparency, and the impact of automated feedback on trainee development, require careful attention.Conclusions: AI use in UK surgical training is emerging but currently driven largely by pilot studies and policy direction rather than high-quality outcome evidence. Major gaps include multi-centre validation, curriculum integration, standardised assessment frameworks, and equitable access to AI-enabled systems. Future UK research should prioritise structured validation studies and national coordination to define effective, scalable, and safe AI tools that can enhance surgical education across the NHS.

As artificial intelligence (AI) is becoming more integrated into the field of healthcare, medical students need to learn foundational AI literacy. Yet, traditional, descriptive teaching methods of AI topics are often ineffective in engaging the learners. This article introduces a new application of cinema to teaching AI concepts in medical education. With meticulously chosen movie clips from "Enthiran (Tamil)/Robot (Hindi)/Robo (Telugu)" movie, the students were introduced to the primary differences between artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial super intelligence (ASI). This method triggered encouraging responses from students, with learners indicating greater conceptual clarity and heightened interest. Film as an emotive and visual medium not only makes difficult concepts easy to understand but also encourages curiosity, ethical consideration, and higher order thought. This pedagogic intervention demonstrates how narrative-based learning can make abstract AI systems more relatable and clinically relevant for future physicians. Beyond technical content, the method can offer opportunities to cultivate critical engagement with ethical and practical dimensions of AI in healthcare. Integrating film into AI instruction could bridge the gap between theoretical knowledge and clinical application, offering a compelling pathway to enrich medical education in a rapidly evolving digital age.NEW & NOTEWORTHY This article introduces a new learning strategy that employs film to instruct artificial intelligence (AI) principles in medical education. By introducing clips the from "Enthiran (Tamil)/Robot (Hindi)/Robo (Telugu)" movie to clarify artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial super intelligence (ASI), the approach converted passive learning into an emotionally evocative and intellectually stimulating experience. Students experienced enhanced comprehension and increased interest in artificial intelligence. This narrative-driven, visually oriented process promises to incorporate technical and ethical AI literacy into medical curricula with enduring relevance and impact.

This review focuses on the critical role of human factors in the integration of artificial intelligence (AI) into urology. Whilst AI holds promise for enhancing diagnostics, surgical precision and personalised care, its success depends considerably on the cognitive, physical and psychosocial dimensions of human interaction with these systems. Key human factors, such as cognitive load, trust, collaboration and communication, directly influence the adoption and effectiveness of AI technologies. For instance, clinicians must balance leveraging AI's insights with maintaining critical thinking to avoid automation bias. The design and ergonomics of AI tools, and their seamless integration into clinical workflows, play pivotal roles in optimising efficiency and minimising disruptions. Psychosocial elements like transparency, team dynamics and patient-centric communication are vital to fostering trust and ensuring ethical use of AI in sensitive contexts. Training and continuous professional development tailored to human factors are essential to empower clinicians to work effectively alongside AI. Ethical considerations, including accountability and fairness, further emphasise the need for transparent processes and diverse datasets to address algorithmic biases. This review highlights the path toward a responsible and patient-centred integration of AI in urology by prioritising human factors, ultimately bridging the gap between technological innovation and compassionate healthcare delivery.

Artificial intelligence (AI) is the simulation of human intelligence in machines that are programmed to think and learn like humans. AI has the potential to revolutionise the way that healthcare professionals diagnose, treat, and manage conditions affecting the female reproductive system. Machine learning (ML) is a subset of AI which deals with the development of algorithms and statistical models that enable computers to learn from and make predictions or decisions without being explicitly programmed to do so. Deep learning (DL) is a subfield of ML that utilises neural networks with multiple layers, known as deep neural networks (DNNs), to learn from data. DNNs are inspired by the structure and function of the human brain and are capable of automatically learning high-level features from raw data, such as images, audio and text. DL has been very successful in various applications such as image and speech recognition, natural language processing and computer vision. ML algorithms can be divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning algorithms are trained on a labelled dataset, where the desired output (label) is already known. Unsupervised learning algorithms are trained on an unlabelled dataset and are used to discover patterns or relationships in the data. Reinforcement learning algorithms are trained using a trial-and-error approach, where the agent receives a reward or penalty for its actions. The goal of reinforcement learning is to learn a policy that maximises the expected reward over time. AI and ML are increasingly being applied in the field of obstetrics and gynaecology, with the potential to improve diagnostic accuracy, patient outcomes, and efficiency of care. AI has been applied to the field of medicine for several decades. One of the earliest examples of AI in medicine was the development of MYCIN in the 1970s, a computer program that could diagnose bacterial infections and recommend appropriate antibiotic treatments. MYCIN was developed by a team at Stanford University led by Edward Shortliffe, and its success demonstrated the potential of AI in medical decision making. In the 1980s, AI-based expert systems such as DXplain, developed at Massachusetts General Hospital, were used to assist in the diagnosis of diseases. These early AI systems were based on rule-based systems and were limited in their capabilities. One of the earliest examples of AI was the development of computer-aided diagnostic systems for ultrasound images in the 1970s and 1980s. These systems were designed to assist radiologists in identifying fetal anomalies and other conditions. In recent years, there has been a renewed interest in the use of AI in obstetrics and gynaecology, driven by advances in ML and the availability of large amounts of data. One of the primary areas in which AI and ML are being used in obstetrics and gynaecology is in the analysis of imaging data, such as ultrasound and magnetic resonance imaging. AI algorithms can be trained to automatically identify and classify different structures in the images, such as the placenta or fetal organs, with high accuracy. Another area of focus is the use of AI to predict preterm birth. Researchers have used ML algorithms to analyse data from electronic health records and identify patterns that are associated with preterm birth. By analysing large datasets of patient information and outcomes, AI algorithms can identify patterns and risk factors that may not be apparent to human analysts. This can help to improve the prediction of obstetric outcomes and guide clinical decision making. In recent years, AI has also been applied in obstetrics and gynaecology for real-time monitoring of high-risk pregnancies and identifying fetal distress. These systems use ML algorithms to analyse data from fetal heart rate monitors and identify patterns that are associated with fetal distress. AI and ML are also being used to develop new tools for the management of gynaecological conditions, such as endometriosis and fibroids. These tools can be used to predict the progression of the disease and guide treatment decisions. One example of the use of AI in benign gynaecology is the development of computer-aided diagnostic systems for endometriosis. These systems use ML algorithms to analyse images of the pelvic region and identify the presence of endometrial tissue, which can be a sign of endometriosis. Another area where AI and ML are being applied is in the management of fibroids. ML algorithms are being used to analyse imaging data and predict the growth and behaviour of fibroids, which can aid in the development of personalised treatment plans. In the field of oncology, AI is being used to improve the accuracy and speed of cancer diagnosis. AI algorithms can analyse images of tissue samples to identify the presence of cancer cells and predict the likelihood of a positive outcome following treatment. AI algorithms can be trained to analyse images from pelvic scans and identify signs of ovarian cancer with high accuracy. In addition to these specific applications, AI and ML are also being used to improve the efficiency and organisation of care in obstetrics and gynaecology. For example, by analysing large amounts of clinical data, AI algorithms can be used to identify patients at high risk of complications, prioritise them for care and ensure that they receive the appropriate level of care in a timely manner. AI and ML have the potential to revolutionise the field of fertility and in vitro fertilisation (IVF). By using data from large patient populations, AI and ML algorithms can help identify patterns and predict outcomes that would be difficult for human experts to discern. This can lead to improvements in diagnosis, treatment planning, and overall success rates for patients undergoing IVF. One area where AI and ML are being applied is in the selection of embryos for transfer during IVF. By analysing images of embryos, AI and ML algorithms can predict which embryos are most likely to result in a successful pregnancy. Another area where AI and ML have shown potential is in the optimisation of culture conditions for embryos. This has the potential to improve the survival and development of embryos, leading to higher pregnancy rates. AI and ML are also being used to improve the timing of embryo transfer during IVF. By analysing data from patient medical histories, AI and ML algorithms can predict the optimal time for transfer to increase the chances of successful pregnancies. In addition to these applications, AI and ML are being used in other areas of fertility and IVF to improve patient outcomes. For example, AI and ML are being used to predict the likelihood of ovarian reserve, predict ovulation timing, and improve the efficiency and cost-effectiveness of fertility clinics. AI and ML are rapidly evolving fields that have the potential to revolutionise the field of surgery. These technologies can be used to assist surgeons in a variety of ways, from pre-operative planning to real-time guidance during procedures. One of the key areas where AI and ML are being applied in surgery is in image analysis. For example, algorithms can be used to automatically segment and identify structures in medical images, such as tumours or blood vessels. This can help surgeons plan procedures more accurately and reduce the risk of complications. Another area where AI and ML are being used in surgery is in the development of robotic systems. These systems can be programmed to perform specific tasks, such as suturing or cutting tissue, with a high degree of precision and accuracy. In addition, robotic systems can be equipped with sensors that provide real-time feedback to the surgeon, which can help to improve the outcome of the procedure. These systems can be programmed with advanced algorithms that allow them to make precise incisions, control bleeding, and minimise tissue damage. AI and ML can also be used to improve the efficiency and safety of surgical procedures. For example, algorithms can be trained to analyse data from vital signs monitors, such as heart rate and blood pressure, and alert surgeons to potential complications in real-time. AI and ML are also being used to assist with post-operative care. For example, algorithms can be used to analyse patient data and predict which patients are at risk of complications, such as infection or bleeding, allowing surgeons to take preventative measures. Overall, AI and ML have the potential to significantly improve the field of surgery by increasing accuracy and precision, reducing the risk of complications, and improving patient outcomes. As the technology continues to advance, it is likely that we will see an increasing number of AI-assisted surgical systems and applications in clinical practice. In gynaecology specifically, there is a scarcity of data and diversity in the data. This can lead to AI models that are not generalisable to certain populations or that make incorrect predictions for certain groups of patients. Overall, AI has the potential to improve the diagnosis and management of obstetrics and gynaecology conditions, and many studies have shown that AI systems can perform at least as well as human experts in several areas. However, it is important to note that AI and ML are still in the early stages of development in obstetrics and gynaecology and more research is needed to fully understand their potential benefits and limitations. Some of the key challenges facing the field include developing AI systems that can explain their decisions, improving the robustness of AI systems to adversarial attacks, and developing AI systems that can operate in a wide range of environments. However, it is important to note that AI is a complementary tool to the obstetrics and gynaecology specialist and it is not meant to replace human expertise. The preceding text is entirely a product of an AI system. The preceding review, Artificial Intelligence in Gynaecology: An Overview was composed and written by an evolutionary AI system, ChatGPT (Chat Generative Pre-trained Transformer). ChatGPT is an AI chatbot underpinned by the GPT architecture, an autoregressive language model that uses DL to produce human-like text. The system was trained on a dataset of over 500 GB of text data derived from books, articles, and websites prior to 2021. The system can engage in responsive dialogue, generate computer code, and produce coherent and fluent text.1 ChatGPT was conceived by OpenAI, an AI laboratory based in San Francisco, California, founded by Elon Musk and Sam Altman in 2015. Since its public release on November 30, 2022, the potential for use and misuse has exponentially grown,2 ultimately leading to the prohibition of the utilisation of AI systems by multiple organisations, including schools and universities. Prompted by this interest in AI, the aim of this study was to assess the capacity of ChatGPT to generate a scientific review. In January 2023, a multidisciplinary study group was assembled to develop the study protocol, confirm the methodology and approve the topic. This research was exempt from ethics review under National Health and Medical Research Council guidelines.3 ChatGPT was instructed to generate an narrative review based on dialogue with the lead author, AY. The input was informed by collaborative meetings of the study group over the study period. The study group nominated the topic, 'Artificial Intelligence in Gynaecology', but ChatGPT generated the title, structure and content for this paper. The study group defined the input parameters for ChatGPT and each AI output was reviewed by the authors for consistency and context, informing the next input. The dialogue thus became increasingly specific and refined in each iteration, as the initial general outline was expanded to include specific subheadings, academic language and academic references. The review was finalised from the ChatGPT output through an explicit composition protocol, limiting assembly to cut and paste, deletion to whole sentences (but not words) and conversion to Australian English. No grammatical or syntax correction was performed. The AI output was cross-referenced and verified by the study group. In this study, ChatGPT generated 7112 words in over 15 iterations, including 32 references. The output was restricted to the final review of 1809 words and nine unique references after removing duplicates4 and incorrect references (19). The final paper was submitted for blinded peer review. Thus, this study has demonstrated the capacity of an AI system, such as ChatGPT, to generate a scientific review through human academic instruction. AI is anticipated to expand the boundaries of evidence-based medicine through the potential of comprehensive analysis and summation of scientific publications. However, unlike systematic reviews or meta-analyses governed by explicit methodology, AI systems such as ChatGPT are the product of DL algorithms that are dependent upon the quality of the input to train the AI. Consequently, unlike systematic reviews, AI systems are bound by the bias, breadth, depth and quality of the training material. A dedicated medical AI would therefore be trained on an appropriate data set, such as the National Library of Medicine Medline/PubMed database. However, the volume of data is challenging: in 2022 alone, there were over 33 million citations equating to a dataset of almost 200 Gb for the minimum dataset. In contrast, ChatGPT has no external reference capabilities, such as access to the internet, search engines or any other sources of information outside of its own model. If forced outside of this framework, ChatGPT may generate plausible-sounding but incorrect or nonsensical responses.4 Most notably, pushing the AI to include references leads the system to generate bizarre fabrications.5 Our paper demonstrated that only 28% (9/32) of the references were authentic, although better than the 11% reported in a recent paper.6 In contrast to human writing, AI-generated content is more likely to be of limited depth, contain factual errors, fabricated references and repeat the instructions used to seed the output.7 The latter results in a formulaic language redundancy that all but identifies AI content. The human authors thus echo the conclusion of ChatGPT that AI is a complementary tool to the specialist and not meant to replace human expertise. For the moment. The authors report no conflicts of interest.

Position statement from the society of University surgeons, surgical education committee: Artificial intelligence in surgical training for medical students, residents, and fellows.

The present study aims to examine the ethical considerations about the exploration of Artificial Intelligence technology. As the field of artificial intelligence (AI) continues to grow, it is important to examine the ethical implications of its use. The Global AI Technology Acceptance Model and Innovation Resistance Theory are two theoretical frameworks that can be used to understand the impact of AI on ethical considerations. By analyzing these frameworks, we can better understand the factors contributing to adopting AI and how ethical concerns can be addressed. This paper aims to explore the intersection of these two theories and their potential implications for ethical considerations in the development and deployment of artificial intelligence. This research contributes to a deeper understanding of the ethical considerations surrounding the use of AI. It provides insights into how we can ensure that AI is used responsibly and ethically. The result of this study is of great importance given the rapid pace of technological advancement and the potential for AI to significantly impact society.

This scholarly investigation delves into the transformative impact of Artificial Intelligence (AI) on enhancing customer experience in the business realm. The study's purpose was to meticulously examine the integration, evolution, and strategic implications of AI in business operations, particularly in customer engagement. A comprehensive literature review and detailed case study analysis constituted the core methodology, focusing on peer-reviewed articles and practical examples from diverse business sectors. This approach facilitated a multi-dimensional exploration, capturing both the technological advancements in AI and the associated implementation challenges within various business contexts. Central findings from this research underscore AI's evolution from an emerging technological tool to a fundamental component in customer-centric business strategies. AI's capabilities in personalizing customer interactions, automating support systems, and leveraging predictive analytics have revolutionized business-customer dynamics. However, this evolution is not without its challenges, including data privacy concerns, ethical considerations, and the need for skilled AI expertise. The study concludes that AI is a strategic asset, necessitating thoughtful integration into business models. It emphasizes the importance of a collaborative approach, where AI specialists and industry experts work synergistically to tailor AI solutions to specific business needs. Ethical considerations and maintaining customer trust are highlighted as pivotal in AI deployment strategies. The study recommends continuous innovation, investment in AI infrastructure and talent, and adherence to ethical AI practices. These measures are essential for businesses to enhance customer experiences and drive sustainable growth in the digital age
 Keywords: Artificial Intelligence, Customer Experience, Business Strategy, AI Integration, Ethical Considerations.

Artificial Intelligence (AI) has revolutionized modern surgery by enhancing every stage of patient care, from preoperative planning to postoperative monitoring. This paper explores the impact of AI in conjunction with other technologies in surgical procedures, emphasizing their empirical basis and integration into clinical practice. AI s role in facilitating personalized treatment planning through a comprehensive analysis of patient data and imaging studies, utilizing techniques like natural language processing (NLP) to extract critical insights, reassures us of its positive impact on patient care. Real time decision support systems powered by AI improve surgical precision, enabling surgeons to navigate complex procedures with enhanced accuracy and efficiency. Furthermore, AI driven surgical robotics exemplify the precision achievable with these technologies, enabling minimally invasive procedures that minimize patient trauma and expedite recovery. Integrating AI with computer vision further enhances surgical capabilities by allowing machines to interpret visual data autonomously, like human perception. Convolutional Neural Networks (CNNs) are pivotal in image recognition and analysis, supporting tasks from anatomical landmark identification to surgical planning. Augmented Reality (AR), when combined with AI, enriches surgical practice by overlaying digital information onto real world views, aiding in intraoperative guidance and educational training. Devices like Apple s Vision Pro (AVP) headset showcase the potential of mixed reality technologies in enhancing surgical precision. AVP s integration of spatial computing and AI algorithms allows for real time data analysis and decision support, transforming surgical education and procedural outcomes. Despite the transformative potential, challenges, including ethical considerations, data privacy, and regulatory frameworks, must be addressed to ensure the responsible deployment of AI in surgical settings. These challenges include mitigating biases in AI algorithms and ensuring equitable access to advanced technologies across diverse surgical specialties. The dynamic nature of AI in surgery necessitates continued research and development to refine AI applications, optimize surgical workflows, and improve patient outcomes globally. In com bination with contemporary technologies, AI represents a paradigm shift in surgical practice, offering unprecedented opportunities to enhance patient care through personalized, precise, and efficient interventions. AI s ongoing evolution and integration in surgery promise to reshape healthcares future, advancing clinical practice and medical education toward safer, more effective, and inclusive healthcare delivery systems.

Artificial Intelligence (AI) is increasingly discussed as a tool that can support speech and language therapy (SLT). However, clinical adoption of AI requires improved AI literacy among clinicians. AI is a rapidly evolving and often inconsistently defined field that can be difficult to navigate. Despite the definition provided by the EU AI Act, AI terminology can feel abstract for non-technical readers. To provide a foundational understanding of AI tailored for SLTs, by translating complex concepts into accessible language and organising them across three levels: (i) AI techniques (how AI works); (ii) AI capabilities (what AI can do) and (iii) clinical applications (how AI can support SLT). This tutorial is informed by foundational AI literature, established AI taxonomies, relevant SLT literature and regulatory and ethical guidelines. Clinical analogies are used to explain technical concepts, with additional technical detail signposted where relevant. Existing and conceptual examples illustrate the relevance of AI across paediatric SLT practice. This tutorial provides: (i) a clinician-focussed interpretation of the EU AI Act definition; (ii) an organisation of key AI concepts into techniques, capabilities and clinical applications; (iii) a production-line model for mapping clinical needs to AI design choices and (iv) a practice-focussed discussion of ethical and regulatory considerations. AI is best understood as a set of techniques that enable specific capabilities, which in turn support clinical applications. This tutorial promotes the safe, ethical and accountable use of AI as a tool that can support rather than replace clinicians. What is already known on this subject Current Artificial Intelligence (AI) literature is typically designed for technical audiences, making it difficult for clinicians to interpret. This can hinder the effective and responsible integration of AI into clinical practice. What this paper adds to the existing knowledge This tutorial provides a clinician-focussed explanation of AI, structured across three levels: (i) AI techniques (how AI works); (ii) AI capabilities (what AI can do) and (iii) clinical applications (how AI supports practice) in paediatric speech and language therapy. It also addresses key challenges, ethical considerations and regulatory requirements relevant to clinical contexts. What are the potential or actual clinical implications of this work? This tutorial lays the groundwork for informed engagement with emerging AI tools. It prepares clinicians to evaluate how different AI techniques and capabilities may support core clinical tasks (e.g., assessment, therapy planning and delivery).

Exploring the role of artificial intelligence in orthopedic medical education: A narrative review.

As Artificial Intelligence (AI) continues to play an increasingly pivotal role in medical decision support systems, the ethical implications of its integration into healthcare practices demand comprehensive examination. This review delves into the ethical considerations surrounding AI-enhanced medical decision support systems, aiming to provide insights into the challenges, existing frameworks, exemplary practices, and emerging trends in this rapidly evolving field. The significance of ethical considerations is underscored by the patient-centric focus, emphasizing the impact of AI on patient outcomes and the delicate balance between technological advancements and patient welfare. Trust and transparency emerge as critical pillars, exploring the role of trust in medical decision-making and the imperative of ensuring transparency in AI algorithms to foster confidence among healthcare professionals and patients. Ethical challenges, including privacy and confidentiality concerns, biases in AI algorithms, and issues related to informed consent, are thoroughly examined. Strategies for safeguarding patient data, mitigating biases, and transparently communicating with patients are explored to address these challenges. The role of accountability and responsibility is delineated, defining the ethical responsibilities of both healthcare professionals and AI developers. The review surveys existing ethical frameworks in healthcare AI and evaluates their applicability and effectiveness. Additionally, it highlights recent proposals for ethical guidelines, emphasizing the need to integrate ethical considerations into the entire development life cycle of AI-enhanced medical decision support systems. Case studies and exemplary practices from healthcare institutions implementing ethical AI serve to illustrate real-world applications and offer insights into best practices. The evolving landscape of ethical AI research is explored, showcasing ongoing initiatives and potential innovations that hold promise for addressing ethical challenges in the future. This review underscores the paramount importance of ethical considerations in the integration of AI into medical decision support systems. It provides a comprehensive overview of current challenges, existing frameworks, exemplary practices, and emerging trends, emphasizing the ongoing need for vigilance and ethical governance to ensure the responsible and beneficial deployment of AI in healthcare.

The technical breakthrough of artificial intelligence (AI) in the field of oncology has moved from the laboratory to the clinic, but the realization of its social value is still facing the "last mile" dilemma. According to the WHO, there are more than 19 million new cancer cases worldwide every year, but the algorithmic advantages of AI are in sharp contrast to the uneven distribution of resources: while high-income countries are using AI to optimize personalized treatment programs, low-income regions are difficult to enjoy the technical dividends due to the lack of data. This work takes the " Technology-Ethics-Fairness" framework as the starting point to explore how to build a more inclusive AI oncology research ecology through interdisciplinary cooperation. Despite the outstanding performance of AI in the fields of tumor image recognition and genomics analysis, most studies focus on technical performance optimization and ignore the impact of social and cultural differences on the implementation of algorithms. For example, the driver gene mutation characteristics of lung cancer in Asian populations are significantly different from those in Europe and the United States, but the proportion of non-European ancestry samples in the public database is less than 10%, which leads to bias when the model is applied across regions. Furthermore, the inherent "black box" nature of AI decision-making exacerbates the crisis of trust between doctors and patients, especially in areas with limited medical resources, where technical authority may override clinical experience. To foster responsible and equitable AI in oncology, we propose three key pillars so that AI research can better serve society: (1) Data Equity: Establishing a global federated learning consortium for privacy-preserving, multi-omic data sharing to enable cross-regional model training. (2) Interpretability & Trust: Developing "decision traceability" tools that dynamically link AI outputs to clinical guidelines and supporting evidence. (3) Proactive Ethics: Integrating ethical impact assessments, informed by frameworks like the EU AI Act, into clinical trial design, including explicit metrics for equity and bias. The ultimate value of AI should not stop at improving the efficiency of diagnosis and treatment but also reshape the global collaboration network of cancer research. It is recommended to establish an international certification standard of "AI for Oncology," covering the dimensions of data representativeness, algorithm transparency, and cross-cultural adaptability. At the same time, bridging the technology gap through immersive medical education can help doctors in underdeveloped countries or regions to practice AI-assisted decision-making on 3D tumor models. As AI evolves from "technology enabler" to "ecological builder," cancer research will break through the boundaries of regions and disciplines and realize exponential growth of social value. We look forward to seeing more solutions that integrate technological innovation and humanistic care in the future. Citation Format: Zhicheng Du, Lijin Lian, Wenji Xi, Yu Zheng, Gang Yu, Hui-Yan Luo, Peiwu Qin. Artificial intelligence enables the ethical reconstruction and social value realization of global cancer research: From technological innovation to humanistic care [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B024.