Search

Artificial Intelligence in Biomedical and Modern Healthcare Informatics provides a deeper understanding of the current trends in AI and machine learning within healthcare diagnosis, its practical approach in healthcare, and gives insight into different wearable sensors and its device module to help doctors and their patients in enhanced healthcare system. The primary goal of this book is to detect difficulties and their solutions to medical practitioners for the early detection and prediction of any disease. The 56 chapters in the volume provide beginners and experts in the medical science field with general pictures and detailed descriptions of imaging and signal processing principles and clinical applications. With forefront applications and up-to-date analytical methods, this book captures the interests of colleagues in the medical imaging research field and is a valuable resource for healthcare professionals who wish to understand the principles and applications of signal and image processing and its related technologies in healthcare. - Discusses fundamental and advanced approaches as well as optimization techniques used in AI for healthcare systems - Includes chapters on various established imaging methods as well as emerging methods for skin cancer, brain tumor, epileptic seizures, and kidney diseases - Adopts a bottom-up approach and proposes recent trends in simple manner with the help of real-world examples - Synthesizes the existing international evidence and expert opinions on implementing decommissioning in healthcare - Promotes research in the field of health and hospital management in order to improve the efficiency of healthcare delivery systems

Implementation of artificial intelligence (AI) in radiology is an important topic of discussion. Advances in AI—which encompass machine learning, artificial neural networks, and deep learning—are increasingly being applied to diagnostic imaging. While some posit radiologists are irreplaceable, certain AI proponents have proposed to'stop training radiologists now.'By compiling perspectives from experts from various backgrounds, this book explores the current state of AI efforts in radiology along with the clinical, financial, technological, and societal perspectives on the role and expected impact of AI in radiology.

Digitalization and the Transformation of the Healthcare Sector provides a compilation of critical aspects that collectively serve to underscore the potential digital technologies advances can bring to healthcare delivery. It discusses the potential of e-health and lays out a clear road map and vision for achieving high quality, high value care for all. Covering topics such as 5G technology, metaverse, and telemedicine, this book is an excellent resource for researchers, academicians, clinicians, allied health workers, managers in healthcare, policymakers, graduate and postgraduate students, and more.

'In mental health care, artificial intelligence (AI) tools can enhance diagnostic accuracy, personalize treatment plans, and provide support through virtual therapy and chatbots that offer real-time assistance. These technologies can help identify early signs of mental health issues by analyzing patterns in speech, behavior, and physiological data. However, the integration of AI also raises concerns about privacy, data security, and the potential for algorithmic bias, which could impact quality of care. As AI continues to evolve, its role in psychological well-being and healthcare will depend on addressing these ethical and practical considerations while harnessing its potential to improve mental health outcomes and streamline healthcare delivery. AI Technologies and Advancements for Psychological Well-Being and Healthcare discusses the latest innovations in AI that are transforming the landscape of mental health and healthcare services. This book explores how AI applications, such as machine learning algorithms and natural language processing, are enhancing diagnostic accuracy, personalizing treatment options, and improving patient outcomes. Covering topics such as behavioral artificial intelligence, medical diagnosis, and precision medicine, this book is an excellent resource for mental health professionals, healthcare providers and administrators, AI and data scientists, academicians, researchers, healthcare policymakers, and more.'--

This book mainly focuses on the application of AI technology in the field of ECG, and details the theoretical, practical, and evidence-based aspects of this technology. It extensively reviews the latest research reports, and discusses the application of artificial intelligence in the diagnosis and treatment of cardiovascular diseases. This book focuses on the application of artificial intelligence in the diagnosis and treatment of cardiovascular diseases, and discusses in depth its clinical application in the diagnosis and treatment of coronary heart disease, heart failure, arrhythmia, cardiac pacing, hypertension, myocardial disease, heart valve disease, electrolyte disorders, and other diseases. It not only provides an overview of medical artificial intelligence but also richly demonstrates its clinical practice, which will help cardiovascular doctors, electrocardiogram diagnostic doctors, or medical students to understand the basic theories, applications, and development direction of this field.

Intelligent Computing Techniques in Biomedical Imaging provides comprehensive and state-of-the-art applications of Computational Intelligence techniques used in biomedical image analysis for disease detection and diagnosis. The book offers readers a stepwise approach from fundamental to advanced techniques using real-life medical examples and tutorials. The editors have divided the book into five sections, from prerequisites to case studies.Section I presents the prerequisites, where the reader will find fundamental concepts needed for advanced topics covered later in this book. This primarily includes a thorough introduction to Artificial Intelligence, probability theory and statistical learning. The second section covers Computational Intelligence methods for medical image acquisition and pre-processing for biomedical images. In this section, readers will find AI applied to conventional and advanced biomedical imaging modalities such as X-rays, CT scan, MRI, Mammography, Ultrasound, MR Spectroscopy, Positron Emission Tomography (PET), Ultrasound Elastography, Optical Coherence Tomography (OCT), Functional MRI, Hybrid Modalities, as well as pre-processing topics such as medical image enhancement, segmentation, and compression. Section III covers description and representation of medical images. Here the reader will find various categories of features and their relevance in different medical imaging tasks. This section also discusses feature selection techniques based on filter method, wrapper method, embedded method, and more.The fourth section covers Computational Intelligence techniques used for medical image classification, including Artificial Neural Networks, Support Vector Machines, Decision Trees, Nearest Neighbor Classifiers, Random Forest, clustering, extreme learning, Convolution Neural Networks (CNN), and Recurrent Neural Networks. This section also includes a discussion of computer aided diagnosis and performance evaluation in radiology.The final section of Intelligent Computing Techniques in Biomedical Imaging provides readers with a wealth of real-world Case Studies for Computational Intelligence techniques in applications such as neuro-developmental disorders, brain tumor detection, breast cancer detection, bone fracture detection, pulmonary imaging, thyroid disorders, imaging technologies in dentistry, diagnosis of ocular diseases, cardiovascular imaging, and multimodal imaging. - Introduces Fourier theory and signal analysis tailored to applications in optical communications devices and systems - Provides strong theoretical background, making it a ready resource for researchers and advanced students in optical communication and optical signal processing - Starts from basic theory and then develops descriptions of useful applications

Artificial Intelligence Technology in Healthcare: Security and Privacy Issues focuses on current issues with patients'privacy and data security including data breaches in healthcare organizations, unauthorized access to patients'information, and medical identity theft. It explains recent breakthroughs and problems in deep learning security and privacy issues, emphasizing current state-of-the-art methods, methodologies, implementation, attacks, and countermeasures. It examines the issues related to developing artifiicial intelligence (AI)-based security mechanisms which can gather or share data across several healthcare applications securely and privately. Features: Combines multiple technologies (i.e., Internet of Things [IoT], Federated Computing, and AI) for managing and securing smart healthcare systems. Includes state-of-the-art machine learning, deep learning techniques for predictive analysis, and fog and edge computing-based real-time health monitoring. Covers how to diagnose critical diseases from medical imaging using advanced deep learning-based approaches. Focuses on latest research on privacy, security, and threat detection on COVID-19 through IoT. Illustrates initiatives for research in smart computing for advanced healthcare management systems. This book is aimed at researchers and graduate students in bioengineering, artificial intelligence, and computer engineering.

Deep Learning in Diabetes Mellitus Detection and Diagnosis focuses on deep learning-based approaches in the field of diabetes mellitus detection and diagnosis, including preprocessing techniques that are an essential part of this subject. This is the first book of its kind to cover deep learning-based approaches in the specific field of diabetes mellitus. This book includes a detailed introductory overview as well as chapters on current applications, preprocessing of data using deep learning, deep learning techniques, complexity, challenges, and future directions. It will be of great interest to researchers and professionals working on diabetes mellitus as well as general medical applications of machine learning.Features: Highlights how the use of deep neural networks-based applications can address new questions and protocols, as well as improve upon existing challenges in diabetes mellitus detection and diagnosis Assists scholars and students who might like to learn about this area as well as others who may have begun without a formal presentation, with no complex mathematical equations Involves exceptional subject coverage and includes the principles needed to understand deep learning

The application of Artificial Intelligence (AI) in the healthcare sector is certain to boost levels of automation and productivity but, paradoxically, it will also increase the availability of “first line competence.” At the same time as demographic trends are affecting demand for health and social care, the technological developments we are seeing make it highly likely that AI will play a decisive role in tackling the challenges our healthcare systems will encounter. This book reveals systemic connections to tackle questions about the potential impact of AI on future challenges in the healthcare sector.Specifically, it develops practical proposals for ways in which AI can be applied to solve these forthcoming issues. It emphasizes the importance of AI in what is known in the literature as human augmentation. The book's innovative perspective is apparent in the way it challenges conventional wisdom in the context of several pressing questions, such as:• What opportunities and challenges could arise from the application of AI in the healthcare sector?• How can the philosophy of medicine, viewed from a systemic perspective, help us to understand, explain, and resolve some of the future challenges in the healthcare sector?• How could AI affect inclusive employment opportunities for people with disabilities?The book also contains an underlying argument to the effect that the rational approach adopted by economists is perhaps less rational when applied to a healthcare sector that is crying out for more “first line competence.”The primary readership will be academic, but the book will also appeal to policymakers, consultants, HR departments, healthcare stakeholders, and related practitioners.

Why the race to apply AI in psychiatry is so dangerous, and how to understand the new tech-driven psychiatric paradigm.AI psychiatrists promise to detect mental disorders with superhuman accuracy, provide affordable therapy for those who can't afford or can't access treatment, and even invent new psychiatric drugs. But the hype obscures an unnerving reality. In The Silicon Shrink, Daniel Oberhaus tells the inside story of how the quest to use AI in psychiatry has created the conditions to turn the world into an asylum. Most of these systems, he writes, have vanishingly little evidence that they improve patient outcomes, but the risks they pose have less to do with technological shortcomings than the application of deeply flawed psychiatric models of mental disorder at unprecedented scale. Oberhaus became interested in the subject of mental health after tragically losing his sister to suicide. In the book, he argues that these new, ostensibly therapeutic technologies already pose significant risks to vulnerable people, and they won't stop there. These new breeds of AI systems are creating a psychiatric surveillance economy in which the emotions, behavior, and cognition of everyday people are subtly manipulated by psychologically savvy algorithms that have escaped the clinic. Oberhaus also introduces readers to the concept of “swipe psychology,” which is quickly establishing itself as the dominant mode of diagnosing and treating mental disorders. It is not too late to change course, but to do so means we must reckon with the nature of mental illness, the limits of technology, and what it means to be human.

Wir leben in einem Zeitalter der Wissensexplosion. Es gab noch nie so viele Informationen für so viele Menschen frei verfügbar wie jetzt. Die Quantität der verfügbaren Daten verändert aber auch das Verständnis davon, was Wissen überhaupt bedeutet. Mit der Flut an Informationen und der generativen Künstlichen Intelligenz ergeben sich neue Herausforderungen, aber auch neue Chancen. Unumstritten ist die Wissensexplosion ein Treiber des Fortschritts in Wirtschaft, Gesellschaft und natürlich auch in der Gesundheitsversorgung. Daten haben das Potenzial, die Lebensqualität und die Lebenserwartung zu verbessern – doch zu welchem Preis, für den Einzelnen und für die Gesellschaft? Das Buch lädt zur Auseinandersetzung mit diesen Fragen ein:

Alzheimer's disease (AD) poses a significant global health challenge, with an estimated 50 million people affected worldwide and no known cure. Traditional methods of diagnosis and prediction often rely on subjective assessments. They are limited in detecting the disease early, leading to delayed intervention and poorer patient outcomes. Additionally, the complexity of AD, with its multifactorial etiology and diverse clinical manifestations, requires a multidisciplinary approach for effective management. AI-Driven Alzheimer's Disease Detection and Prediction offers a groundbreaking solution by leveraging advanced artificial intelligence (AI) techniques to enhance early diagnosis and prediction of AD. This edited book provides a comprehensive overview of state-of-the-art research, methodologies, and applications at the intersection of AI and AD detection. By bridging the gap between traditional diagnostic methods and cutting-edge technology, this book facilitates knowledge exchange, fosters interdisciplinary collaboration, and contributes to innovative solutions for AD management. It also benefits data scientists, engineers, policymakers, and professionals in the pharmaceutical and biotechnology industries. Graduate students interested in healthcare and technology will find accessible information on the latest developments in AI-driven approaches to AD detection and prediction.

In a fully digitized world and hyper-connected society, artificial intelligence (AI) is developing more and more each day. In the aftermath of the Covid-19 pandemic, it seems appropriate to examine the real or imagined progress of AI in terms of human health. Like artificial intelligence, health is a field that involves a wide range of research disciplines. In order to better define and understand these social and technical developments, Al, Healthcare and Law brings together the thoughts and analyses of doctors, lawyers, economists and computer scientists. Through a wide range of original overviews of the issues involved, the book addresses questions such as the development of telemedicine, the use of medical data, the increased human perspective or medical ethics, and takes a multi-disciplinary and accessible approach to questioning the relationship between humans and computers, between the intimate and the machine.

This book provides a fully-fledged exploration of science and technology studies (STS) perspective applied to algorithms developed to support care processes. By concentrating on algorithmic technologies for supporting processes of social and health care, the book intersects topics connected to technoscientific innovation and specifically digital transformation for health care. By offering different attempts of deconstructing algorithmic technologies, the book provides a landmark reference for those interested in undertaking research focused on areas connected to algorithmic decision-making for health care. The book will be an invaluable reference for scholars interested in the STS debate and related fields (e.g.,human–computer interaction, computer supported cooperative work, participatory design, and sociology of health and medicine). This book responds to a growing interest in the application of algorithms'to local and national care systems. The book balances theoretical and empirical analysis bringing together experienced and early-career scholars. This book will be of interest to researchers in STS as well as healthcare professionals and managers as some of the topics covered help to critically reconsider some facets of planning through algorithmic technologies supporting the practice of healthcare and decision-making.

Big Data Analysis and Artificial Intelligence for Medical Sciences Overview of the current state of the art on the use of artificial intelligence in medicine and biology Big Data Analysis and Artificial Intelligence for Medical Sciences demonstrates the efforts made in the fields of Computational Biology and medical sciences to design and implement robust, accurate, and efficient computer algorithms for modeling the behavior of complex biological systems much faster than using traditional modeling approaches based solely on theory. With chapters written by international experts in the field of medical and biological research, Big Data Analysis and Artificial Intelligence for Medical Sciences includes information on: Studies conducted by the authors which are the result of years of interdisciplinary collaborations with clinicians, computer scientists, mathematicians, and engineers Differences between traditional computational approaches to data processing (those of mathematical biology) versus the experiment-data-theory-model-validation cycle Existing approaches to the use of big data in the healthcare industry, such as through IBM's Watson Oncology, Microsoft's Hanover, and Google's DeepMind Difficulties in the field that have arisen as a result of technological changes, and potential future directions these changes may take A timely and up-to-date resource on the integration of artificial intelligence in medicine and biology, Big Data Analysis and Artificial Intelligence for Medical Sciences is of great benefit not only to professional scholars, but also MSc or PhD program students eager to explore advancement in the field.

This book addresses the influence of socioeconomic factors on patients in the COVID-19 analysis and recommends appropriate healthcare methods, including AI tools for patient monitoring and physician assistance. Recent research on healthcare systems during pandemics is discussed, as is the use of AI in the field of mental illness. The chapters in this book address the likelihood of successfully using AI in healthcare, with a focus on coronavirus-related concerns and healthcare systems. The chapters also provide examples of how AI has been used to address social determinants and their impact on public issues during a catastrophic epidemic and discuss how this technology may be used to enhance care services and help the healthcare sector adapt to prospective difficulties.

This book highlights the use of explainable artificial intelligence (XAI) for healthcare problems, in order to improve trustworthiness, performance and sustainability levels in the context of applications.Explainable Artificial Intelligence (XAI) in Healthcare adopts the understanding that AI solutions should not only have high accuracy performance, but also be transparent, understandable and reliable from the end user's perspective. The book discusses the techniques, frameworks, and tools to effectively implement XAI methodologies in critical problems of healthcare field. The authors offer different types of solutions, evaluation methods and metrics for XAI and reveal how the concept of explainability finds a response in target problem coverage. The authors examine the use of XAI in disease diagnosis, medical imaging, health tourism, precision medicine and even drug discovery. They also point out the importance of user perspectives and value of the data used in target problems. Finally, the authors also ensure a well-defined future perspective for advancing XAI in terms of healthcare.This book will offer great benefits to students at the undergraduate and graduate levels and researchers. The book will also be useful for industry professionals and clinicians who perform critical decision-making tasks.

Cutting Edge Artificial Intelligence, Spatial Transcriptomics and Proteomics Approaches to Analyze Cancer, Volume 163 in the Advances in Cancer Research series, highlights new advances in the field, with this new volume presenting interesting topics on the Impact of thermal processing on food flavonoids, Bioinformatics and bioactive peptides from foods: does it work together?, Food off-flavor volatiles generation, characterization and advances in novel strategies for mitigating off-flavor perception, Innovations in Food Packaging for a Sustainable and Circular economy, Upcycling of seafood side streams for circularity, Edible insects in foods, Effect of novel food processing technologies on Bacillus cereus spores, and more. - Contains contributions that have been carefully selected based on their vast experience and expertise on the subject - Includes updated, in-depth, and critical discussions of available information, giving the reader a unique opportunity to learn - Encompasses a broad view of the topics at hand

FOREWORD BY BRAD SMITH, VICE CHAIR AND PRESIDENT OF MICROSOFTDiscover how AI leaders and researchers are using AI to transform the world for the better In AI for Good: Applications in Sustainability, Humanitarian Action, and Health, a team of veteran Microsoft AI researchers delivers an insightful and fascinating discussion of how one of the world's most recognizable software companies is tackling intractable social problems with the power of artificial intelligence (AI). In the book, you'll see real in-the-field examples of researchers using AI with replicable methods and reusable AI code to inspire your own uses. The authors also provide: Easy-to-follow, non-technical explanations of what AI is and how it works Examples of the use of AI for scientists working on mitigating climate change, showing how AI can better analyze data without human bias, remedy pattern recognition deficits, and make use of satellite and other data on a scale never seen before so policy makers can make informed decisions Real applications of AI in humanitarian action, whether in speeding disaster relief with more accurate data for first responders or in helping address populations that have experienced adversity with examples of how analytics is being used to promote inclusivity A deep focus on AI in healthcare where it is improving provider productivity and patient experience, reducing per-capita healthcare costs, and increasing care access, equity, and outcomes Discussions of the future of AI in the realm of social benefit organizations and efforts Beyond the work of the authors, contributors, and researchers highlighted in the book, AI For Good begins with a foreword from Microsoft Vice Chair and President Brad Smith. There, Smith details the Microsoft rationale behind the creation of and continued investment in the AI for Good Lab. The vision is one of hope with AI saving lives in disasters, improving health care globally, and Microsoft's mission to make sure AI's benefits are available to all. An essential guide to impactful social change with artificial intelligence, AI for Good is a must-read resource for technical and non-technical professionals interested in AI's social potential, as well as policymakers, regulators, NGO professionals, and non-profit volunteers.

This book brings together a blend of different areas of machine learning and recent advances in the area. From the use of ML in healthcare to security, this book encompasses several areas related to ML while keeping a check on traditional ML algorithms.Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions describes the predictive analysis and forecasting techniques in different emerging and classical areas using the approaches of ML and AI. It discusses the application of ML and AI in medical diagnostic systems and deals with the security prevention aspects of ML and how it can be used to tackle various emerging security issues. This book also focuses on NLP and understanding the techniques, obstacles, and possible solutions.This is a valuable reference resource for researchers and postgraduate students in healthcare systems engineering, computer science, cyber-security, information technology, and applied mathematics.

Artificial Intelligence for Drug Product Lifecycle Applications explains the use of artificial intelligence (AI) in drug discovery and development paths, including the clinical and postapproval phases. This book gives methods for each of the drug development steps, from the fundamentals to postapproval drug product. AI is a synergistic assembly of enhanced optimization strategies with particular applications in pharmaceutical development and advanced tools for promoting cost-effectiveness throughout the drug lifecycle. Specifically, AI brings together the potential to improve drug approval rates, reduce development costs, get medications to patients faster, and help patients comply with their treatments.Accelerated pharmaceutical development and drug product approval rates will enable larger profits from patent-protected market exclusivity. This book offers the tools and knowledge to create the right AI strategy to extend the landscape of AI applications across the drug lifecycle. It is especially useful for pharmaceutical scientists, health care professionals, and regulatory scientists, as well as advanced students and postgraduates actively involved in pharmaceutical product and process development involving the use of artificial intelligence in drug delivery applications. - Classifies AI methodologies and application examples into different categories representing the various steps of the drug development cycle - Combines timely literature review with clear artworks to improve understanding - Examines deep learning and machine learning in drug discovery

Artificial Intelligence in Clinical Practice: How AI Technologies Impact Medical Research and Clinics compiles current research on Artificial Intelligence within medical subspecialties, helping practitioners with diagnosis, clinical decision-making, disease prediction, prevention, and the facilitation of precision medicine. The book defines the basic concepts of big data and AI in medicine and highlights current applications, challenges, ethical issues, and biases. Each chapter discusses AI applied to a specific medical subspecialty, including primary care, preventive medicine, general internal medicine, radiology, pathology, infectious disease, gastroenterology, cardiology, hematology, oncology, dermatology, ophthalmology, mental health, neurology, pulmonary, critical care, rheumatology, surgery, and OB-GYN. This is a valuable resource for clinicians, students, researchers and members of medical and biomedical fields who are interested in learning more about artificial intelligence technologies and their applications in medicine. Provides the history and overview of the various modalities of AI and their applications within each field of medicine Discusses current AI-based medical research, including landmark trials within each field of medicine Addresses the current knowledge gaps that clinicians commonly face that prevent the application of AI-based research to clinical practice Encompasses examples of specific cases and discusses challenges and biases associated with AI

In the ever-evolving realm of healthcare, Artificial Intelligence in Medicine emerges as a trailblazing guide, offering an extensive exploration of the transformative power of Artificial Intelligence (AI). Crafted by leading experts in the field, this book sets out to bridge the gap between theoretical understanding and practical application, presenting a comprehensive journey through the foundational principles, cutting-edge applications, and the potential impact of AI in the medical landscape.This book embarks on a journey from foundational principles to advanced applications, presenting a holistic perspective on the integration of AI into diverse aspects of medicine. With a clear aim to cater to both researchers and practitioners, the scope extends from fundamental AI techniques to their innovative applications in disease detection, prediction, and patient care.Distinguished by its practical orientation, each chapter presents actionable workflows, making theoretical concepts directly applicable to real-world medical scenarios. This unique approach sets the book apart, making it an invaluable resource for learners and practitioners alike.Key Features:• Comprehensive Exploration: From deep learning approaches for cardiac arrhythmia to advanced algorithms for ocular disease detection, the book provides an in-depth exploration of critical topics, ensuring a thorough understanding of AI in medicine.• Cutting-Edge Applications: The book delves into cutting-edge applications, including a vision transformer-based approach for brain tumor detection, early diagnosis of skin cancer, and a deep learning-based model for early detection of COVID-19 using chest X-ray images.• Practical Insights: Practical workflows and demonstrations guide readers through the application of AI techniques in real-world medical scenarios, offering insights that transcend theoretical boundaries.This book caters to researchers, practitioners, and students in medicine, computer science, and healthcare technology. With a focus on practical applications, this book is an essential guide for navigating the dynamic intersection of AI and medicine. Whether you are an expert or a newcomer to the field, this comprehensive volume provides a roadmap to the revolutionary impact of AI on the future of healthcare.

Affective computing is the study and development of systems and devices that can recognise human emotions. This can be done using sensing technologies and AI algorithms to process biological signals or facial images to identify the different affective states, such as happiness, anger, fear, surprise, sadness and disgust. This non-invasive technique has applications in healthcare such as emotional impairment detection, mental health assessment, emotional stress assessment, cognitive decline detection, attention deficit disorders, neurodegenerative diseases, neurological disorders, autism spectrum disorder, stress, anxiety or other behavioural assessment.

The Life Science industry has been subject to a digital revolution upending all facets of the value chain with data-driven approaches like Gene Therapy, Personalized Medicine, Advanced Cell and Tissue Engineering, Industry 4.0, Artificial Intelligence, Blockchain, Digital Twins, Internet-of-Medical-Things (IoMT) and Software-as-a-Medical-Device (SaMD). Our edited volume offers comprehensive coverage of the digital disruption sweeping through the industry. From drug discovery to manufacturing and life-cycle management, we explore the profound impact of digitalization. Our authors delve into open innovation, sci-fi prototyping, and the digital transformation of medical imaging and healthcare. This volume goes beyond theory to provide practical insights. We present business-oriented case studies, examine M&A topics, venture capital, and other investor perspectives, all with a strong focus on digitalization's role. Furthermore, we explore the cultural and ethical challenges that arise andthe necessary management changes demanded by this digital revolution.