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Medicine (R0)

Claudio S. Cinà

Medical Ethics

The Surgeon's Perspective

&lt;p&gt;1. Medical Ethics and Its Evolution in Surgical Practice.- 2. Autonomy and Ethical Principles in Surgical Practice.- 3. Confidentiality, Informed Consent, and Patient Autonomy in Surgery.- 4. Autonomy and Beneficence in Assisted Reproductive technology (ART).- 5. Autonomy, Beneficence, Non-maleficence in Organ transplantation.- 6. Autonomy and Beneficence in Genetic Manipulation.- 7. Autonomy and Ethical Dilemmas in Extreme End-of-Life Care.- 8. Justice in Surgical Ethics: Navigating Equity, Accessibility, and Social Responsibility in Surgical Practice.- 9. The Evolution of Surgical Training.- 10. Ethical and Legal Accountability in Surgical and Forensic Medicine: A Medico-Legal and Jurist Perspective.- 11. Ethical Challenges in the Rise of Artificial Intelligence.- 12. Ethical Challenges in Crisis and Epidemic Response.- 13. Personalized Medicine, Data Use, and Ethical Considerations in Surgical Decision-Making.- 14. Ethics in Disability, and Vulnerable Patients.- 15. The Ethics of Medical Research and Experimentation in Surgery.- 16. The Surgical Ethics of Numbers.- 17. Navigating Ethical Codes and Conflicts of Interest in Surgery.- 18. Conclusions.- 19. Opening the Door to Reflection.&lt;/p&gt;

Information Technology and Global Governance

AI has moved from science fiction to the core of modern governance. It has become increasingly integrated into city governance, public services, policymaking, and crisis management, leaving public managers to navigate efficiency gains alongside accountability gaps and algorithmic bias.
 AI for Better Government
 —
 Essential Insights for Academics and Practitioners
 addresses this tension by treating AI governance as a living ecosystem. It also takes an innovative, interdisciplinary, and international approach to explore the rapidly evolving role of AI in public policy and management worldwide.
 
 Written from a pracademic perspective, this book brings together global leading academics and practitioners. Drawing on case studies and real-world examples, it equips readers with tools needed to navigate the complexities of AI adoption for better government outcomes. Each chapter ends with “Questions for Reflection” to fuel active learning and organizational readiness audits. Whether you are a student entering public service, a policymaker weighing decisions, or a researcher mapping digital governance, this book meets you wherever you are.
 Wendy D. Chen
 is Associate Professor of Public Administration at Texas Tech University, USA. She serves as chair of the Section of Science and Technology in Government of American Society of Public Administration (ASPA) and on the AI Task Force of the Network of Schools of Public Policy, Affairs, and Administration (NASPAA).&nbsp;

AI has moved from science fiction to the core of modern governance. It has become increasingly integrated into city governance, public services, policymaking, and crisis management, leaving public managers to navigate efficiency gains alongside accountability gaps and algorithmic bias.
 AI for Better Government
 —
 Essential Insights for Academics and Practitioners
 addresses this tension by treating AI governance as a living ecosystem. It also takes an innovative, interdisciplinary, and international approach to explore the rapidly evolving role of AI in public policy and management worldwide.
 
 Written from a pracademic perspective, this book brings together global leading academics and practitioners. Drawing on case studies and real-world examples, it equips readers with tools needed to navigate the complexities of AI adoption for better government outcomes. Each chapter ends with “Questions for Reflection” to fuel active learning and organizational readiness audits. Whether you are a student entering public service, a policymaker weighing decisions, or a researcher mapping digital governance, this book meets you wherever you are.

Essential Insights for Academics and Practitioners

Artificial Intelligence for Better Government

International Series in Operations Research & Management Science

This book shows how to weld deep learning to the laws that govern heavy-industry assets. Instead of treating sensor readings as detached numbers, the book teaches models to obey conservation of energy, fluid flow, and material stress. The storyline follows three recurring settings—a regional power grid, an offshore drilling head, and a 2000-hectare almond farm—to prove the concept across energy, manufacturing, and agriculture.Readers watch plain neural nets stumble when they predict outputs that break physics, then see PENN correct the error with loss penalties and custom layers that bake in the Navier-Stokes, Hooke, and Fourier equations. In benchmark tests the approach lifts predictive accuracy by up to 30 percent while trimming training energy by two-thirds. Case walk-throughs document a 25 percent cut in turbine downtime, a 50 percent reduction in drilling vibration peaks, and a 15 percent saving in irrigation power.Special features include code snippets ready to paste into PyTorch, side-by-side plots that compare raw and physics-aware outputs, and full-color schematics that trace data from sensor to dashboard. Each chapter ends with a one-page readiness checklist so teams can turn lessons into pilots without stalling in proof-of-concept limbo. A consistent American spelling palette avoids cross-Atlantic edits.By the final page a plant manager, data scientist, or graduate student can design, train, and deploy a model that predicts real-world behavior without ignoring the rules of nature. The payoff is lower energy bills, fewer unplanned shutdowns, and a faster path to net-zero targets—results that speak to both profit and sustainability.&nbsp;

This book shows how to weld deep learning to the laws that govern heavy-industry assets. Instead of treating sensor readings as detached numbers, the book teaches models to obey conservation of energy, fluid flow, and material stress. The storyline follows three recurring settings—a regional power grid, an offshore drilling head, and a 2000-hectare almond farm—to prove the concept across energy, manufacturing, and agriculture.Readers watch plain neural nets stumble when they predict outputs that break physics, then see PENN correct the error with loss penalties and custom layers that bake in the Navier-Stokes, Hooke, and Fourier equations. In benchmark tests the approach lifts predictive accuracy by up to 30 percent while trimming training energy by two-thirds. Case walk-throughs document a 25 percent cut in turbine downtime, a 50 percent reduction in drilling vibration peaks, and a 15 percent saving in irrigation power.Special features include code snippets ready to paste into PyTorch, side-by-side plots that compare raw and physics-aware outputs, and full-color schematics that trace data from sensor to dashboard. Each chapter ends with a one-page readiness checklist so teams can turn lessons into pilots without stalling in proof-of-concept limbo. A consistent American spelling palette avoids cross-Atlantic edits.By the final page a plant manager, data scientist, or graduate student can design, train, and deploy a model that predicts real-world behavior without ignoring the rules of nature. The payoff is lower energy bills, fewer unplanned shutdowns, and a faster path to net-zero targets—results that speak to both profit and sustainability.

The Next Frontier of Artificial Intelligence

Physics-Informed Neural Networks for Industrial Applications

Contributions to Economics

Pasquale L. Scandizzo holds a Ph.D. from the University of California, Berkeley. He is Professor of Political Economy, past Director and Fellow of the Center for Economic and International Studies, Senior Fellow and Member of the Advisory Board of the University of Rome “Tor Vergata” Foundation, President of the Villa Mondragone Economic Development Association, President Emeritus of the Italian Society of Development Economics, and Chairman of OpenEconomics International, a university spin off focusing on project evaluation and economic development. His former institutional positions include the Governing Board of the Italian Institute of Statistics, the Italian Institute for Economic Planning (President), the National Planning Board, the Italian Parliamentary Budget Committee (Senior Adviser), the Ministry of Economics and Finance (Economic Adviser) the World Bank (Senior Economist and Research Coordinator). He has developed widely published methodologies to evaluate public investment programs, by combining option theory, cost benefit analysis and computable general equilibrium methods, and
 
 has been advising the World Bank and other multilateral institutions on the applications of these methodologies to policies of sustainable development.

This study explores the transformative potential of Artificial Intelligence (AI) in the tourism sector, emphasizing its role in enhancing service quality, operational efficiency, and global reach. To assess future impacts, the study uses a Social Accounting Matrix (SAM) and a Computable General Equilibrium (CGE) model built on GTAP 11 data. A scenario analyzing €66 billion in AI-tourism investments across G7 and other nations suggests strong short-term returns, particularly for capital and skilled labor, with the U.S. leading due to favorable infrastructure and market conditions. These findings point to the need for skill development and equitable policy design. AI’s capacity to personalize experiences and optimize operations positions it as a key driver of sustainable growth in tourism, with considerable economic spillovers and transformative potential across global markets.

A General Equilibrium Analysis

Tourism, Structural Features and Artificial Intelligence

Corporate Governance in the Age of Artificial Intelligence

Artificial Intelligence and the Rule of Law

This book offers a timely examination of AI regulation in the era of Industry 5.0, where innovation must be balanced with human well-being, sustainability, and responsible governance. It explores how artificial intelligence is transforming healthcare, environmental stewardship, corporate governance, digital commerce, and emerging technologies while creating new regulatory, ethical, and societal challenges. As AI increasingly influences critical decisions across healthcare systems, innovation ecosystems, educational institutions, and business environments, policymakers and practitioners face growing questions regarding accountability, transparency, safety, trust, privacy, and human rights. Addressing these concerns, this edited volume brings together leading scholars and experts to examine the evolving legal, ethical, and governance frameworks needed to support responsible AI adoption.The chapters cover a diverse range of contemporary topics, including AI-enabled drug discovery, healthcare decision-making, medical liability, patient rights, sustainability governance, climate and environmental regulation, AI in education, intellectual property and innovation, corporate governance, e-commerce regulation, cybersecurity, regulatory sandboxes, and explainable AI in high-risk sectors. Together, these contributions provide interdisciplinary perspectives on how AI can be developed and governed in ways that foster innovation while protecting societal interests.A central theme of the book is the Industry 5.0 vision of human-centric technological progress. The contributors highlight how effective regulation can promote trustworthy AI systems that support sustainable development, strengthen innovation, encourage responsible technological deployment, and create long-term value for society. By bridging law, policy, governance, healthcare, sustainability and technology studies, this volume offers valuable insights into the opportunities and challenges of AI-driven transformation. It provides a forward-looking resource for understanding how regulatory frameworks can enable innovation while ensuring that technological advancement remains ethical, inclusive, and sustainable.This book is intended for researchers, academics, policymakers, regulators, healthcare professionals, technology practitioners, industry leaders, graduate students, and readers interested in artificial intelligence, Industry 5.0, sustainability, healthcare innovation and emerging technologies.

Healthcare, Sustainability, Innovation and Emerging Technologies

AI Regulation in Industry 5.0

Das Buch behandelt das umfangreiche Gebiet der Medizin in einigen Schwerpunktthemen und weist auf ökonomische Zusammenhänge zu diesen Themen hin.&nbsp;Die Notwendigkeit, medizinische mit ökonomischer Forschung und Lehre zu verknüpfen, wird nicht mehr in Frage gestellt, da die Kosten im Gesundheitswesen ausufern. Ein aktuelles Beispiel für überproportional steigende Kosten im Gesundheitswesen sind die Ausgaben für Medikamente, die stärker zunehmen als das Bruttoinlandsprodukt (BIP). Die Kostendiskussion muss daher weiter auf allen Ebenen des Gesundheitswesens vom niedergelassenen Arzt bis zum Krankenhaus, von der pharmazeutischen Industrie bis zu den Spitzenverbänden der Selbstverwaltung geführt werden, da die wirtschaftlichen Konsequenzen einer rationalen medizinischen Versorgung analysiert, kritisch diskutiert, bewertet und transparent dargestellt werden müssen.&nbsp;Es fehlt bisher ein Basislehrbuch der Medizin für Ökonomen, also ein Buch, das medizinische Inhalte auf das für Gesundheitsökonomen relevante Maß begrenzt und dabei versucht, medizinische Maßnahmen im Zusammenhang mit Kostenbewertungen zu veranschaulichen.&nbsp;Das wesentliche Anliegen des vorliegenden Buches ist daher die Verbindung ärztlichen Denkens und Handelns mit ökonomischen Überlegungen, da der Dialog zwischen Ökonomen und Mediziner nur auf der Basis gemeinsamen Wissens effizient geführt werden kann.
 Über den AutorProf. Dr. med. Helmut Brunner
 ist&nbsp;Facharzt für Mikrobiologie und Infektionsepidemiologie, außerplanmäßiger Professor an der Universität Düsseldorf,&nbsp;Lehrbeauftragter an der Universität Wuppertal im Studiengang Gesundheitsökonomie und Gesundheitsmanagement sowie Gutachter für IT-Anwendungen im Gesundheitswesen.

Das Buch behandelt das umfangreiche Gebiet der Medizin in einigen Schwerpunktthemen und weist auf ökonomische Zusammenhänge zu diesen Themen hin.&nbsp;Die Notwendigkeit, medizinische mit ökonomischer Forschung und Lehre zu verknüpfen, wird nicht mehr in Frage gestellt, da die Kosten im Gesundheitswesen ausufern. Ein aktuelles Beispiel für überproportional steigende Kosten im Gesundheitswesen sind die Ausgaben für Medikamente, die stärker zunehmen als das Bruttoinlandsprodukt (BIP). Die Kostendiskussion muss daher weiter auf allen Ebenen des Gesundheitswesens vom niedergelassenen Arzt bis zum Krankenhaus, von der pharmazeutischen Industrie bis zu den Spitzenverbänden der Selbstverwaltung geführt werden, da die wirtschaftlichen Konsequenzen einer rationalen medizinischen Versorgung analysiert, kritisch diskutiert, bewertet und transparent dargestellt werden müssen.&nbsp;Es fehlt bisher ein Basislehrbuch der Medizin für Ökonomen, also ein Buch, das medizinische Inhalte auf das für Gesundheitsökonomen relevante Maß begrenzt und dabei versucht, medizinische Maßnahmen im Zusammenhang mit Kostenbewertungen zu veranschaulichen.&nbsp;Das wesentliche Anliegen des vorliegenden Buches ist daher die Verbindung ärztlichen Denkens und Handelns mit ökonomischen Überlegungen, da der Dialog zwischen Ökonomen und Mediziner nur auf der Basis gemeinsamen Wissens effizient geführt werden kann.

Medizin für Ökonomen

Accounting, Finance, Sustainability, Governance & Fraud: Theory and Application

Emerging Paradigms in FinTech, AI and Blockchain

A Multidisciplinary Approach to KIIT Horizons, Volume 3

Tech, War and the Fight for Humanity

Thundercloud

Risk, Governance and Society

From Data Analytics to Global Risk Governance

Evolution of Anomaly Detection

Turn enterprise AI into a source of strategic insight, not just productivity gains.The AI Gap by leading AI expert and keynote speaker Richard Foster-Fletcher shows business and technology leaders how to unlock greater value from enterprise AI by changing the structures, processes and culture surrounding it. Organizations invest in AI expecting new intelligence and transformative insights. Yet AI is often introduced through procurement, security, legal and compliance systems designed for standard software, restricting its potential before it can deliver on that promise. This book explains why this gap persists and what organizations must change to close it. You'll learn how to: - Identify organizational barriers limiting the value of AI - Move beyond productivity-focused AI implementation - Create conditions where AI can generate deeper insights - Enable employees to explore, experiment and challenge established thinking &gt;The AI Gap equips leaders to rethink AI implementation, create space for experimentation and turn artificial intelligence into a powerful driver of organizational insight and long-term business value. Themes include: enterprise AI, AI implementation, AI strategy, business transformation, organizational culture, AI innovation

Medical Ethics

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