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Responsible Artificial Intelligence Re-engineering the Global Public Health Ecosystem: A Humanity Worth Saving is the first comprehensive book showing how trustworthy AI can revolutionize decolonized global public health. It explains how it works as an ecosystem and how it can be fixed to equitably empower us all to solve the defining crises of our era, from poverty to pandemics, climate to conflicts, debt to divisions. It is written from the first-hand perspective of the world’s first triple doctorate trained physician-data scientist and ethicist who has cared for more than 10,000 patients…mehr

Produktbeschreibung
Responsible Artificial Intelligence Re-engineering the Global Public Health Ecosystem: A Humanity Worth Saving is the first comprehensive book showing how trustworthy AI can revolutionize decolonized global public health. It explains how it works as an ecosystem and how it can be fixed to equitably empower us all to solve the defining crises of our era, from poverty to pandemics, climate to conflicts, debt to divisions. It is written from the first-hand perspective of the world’s first triple doctorate trained physician-data scientist and ethicist who has cared for more than 10,000 patients and authored 5 AI textbooks and more than 400 scientific and ethics papers. This essential resource integrates science, political economics, and ethics to unite our unique cultures, belief systems, institutions, and governments. In doing so, it is meant to give humanity a fighting chance against shared existential threats through cooperation and managed strategic competition for integral sustainable development. Taking seriously diverse voices, perspectives, and insights from the Global North and the Global South, this book uses concrete examples backed up by clear explanations to elucidate the current failures, emerging successes, and societal trends of global public health. It shows how a small number of powerful governments and corporations-amid digitalization, deglobalization, and demographic shifts-dominate global health, and how we can re-engineer a better future for it both societally and technologically. The book spans health breakthroughs in federated data architectures, machine learning, deep learning, swarm learning, quantum computing, blockchain, agile data governance and solidarity, value blocks (of democracies and autocracies), adaptive value supply chains, social networks, pandemics, health financing, universal health coverage, public-private partnerships, healthcare system design, precision agriculture, clean energy, human security, and multicultural global ethics. This book therefore is meant to provide a clear, coherent, and actionable guide equipping students, practitioners, researchers, policymakers, and leaders in digital technology, public health, healthcare, health policy, public policy, political economics, and ethics to generate the solutions that will define humanity’s next era-while recovering what that humanity means, and why it is worth saving.
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Autorenporträt
Dominique J. Monlezun MD, PhD, PhD, MPHUT MD is a practicing physician-data scientist and ethicist. He earned his first AI-focused PhD in Global Health Management & Policy and his second PhD in Bioethics (with the latter recognized by Microsoft as producing the world's top AI ethics doctoral dissertation). He serves as Professor of Cardiology for two American academic medical institutions, Professor of Bioethics for two United Nations-affiliated universities, and the Principal Investigator and Senior Data Scientist and Biostatistician for over 50 research studies associated with Harvard University, the National Institutes of Health, and the European Union, among others. He has authored over 350 peer-reviewed manuscripts and abstracts and book chapters, in addition to the first three comprehensive AI textbooks on bioethics, metaphysics, and public health. He created ML-PSr AI-statistics and Personalist Social Contract ethics after cofounding Culinary Medicine. He has provided medical care for thousands of immigrants, imprisoned, and underserved patients.