05166nam 2200613 a 450 991098462110332120221129160846.09781975197957197519795X97819751979401975197941(CKB)5720000000120557(UtOrBLW)22882503(UtOrBLW)9781975197933(MiAaPQ)EBC31246316(Au-PeEL)EBL31246316(OCoLC)1428905305(EXLCZ)99572000000012055720221129d2024 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierArtificial intelligence for improved patient outcomes principles for moving forward with rigorous science /Daniel W. Byrne1st ed.Philadelphia Wolters Kluwer20241 online resource (272 p.) col. ill9781975197933 1975197933 Includes bibliographical references and index.Overview : How Artificial Intelligence Will Improve Health -- Randomization : The "Secret Sauce" -- Evaluation : The Facts Matter. Pseudo-Innovation vs Real Innovation -- Synergy : Building a Successful Clinician-Computer Collaboration -- Fairness : Addressing the Ethical, Regulatory, and Privacy Issues -- Modeling : An Overview of Predictive Modeling, Neural Networks, and Deep Learning -- EHRs : Exporting, Cleaning, Managing Datasets, and Integrating Models into the Electronic Health Record -- Resistance : Understanding and Overcoming the Resistance to AI, Randomization, and Change -- Execution : Increasing the Odds of Future Success -- Integration : Building a Learning Health Care System With Pragmatic AI Trials -- Streamlining : Reducing Waste and Lowering Costs in Health Care -- Complications : Predicting and Preventing Hospital Complications -- Prevention : Identifying Diseases With Predictive Models -- Precision Medicine : AI to Improve Health Screenings and Treatments -- Drugs and Devices : Using AI to Improve Pharmaceutical and Medical Device Development and Applications -- Medical Literature : AI and Information Overload -- Imaging : Medical Imaging and Strategies for Assessing Patient Impact -- Pandemics : Using AI Tools to Improve Health Outcomes in a Pandemic -- Careers : How to Build a Career Around AI in Medicine by Turning This Playbook Into a Reality."Artificial Intelligence for Improved Patient Outcomes provides new, relevant, and practical information on what AI can do in healthcare and how to assess whether AI is improving health outcomes. With clear insights and a balanced approach, this innovative book offers a one-stop guide on how to design and lead pragmatic real-world AI studies that yield rigorous scientific evidence-all in a manner that is safe and ethical. Daniel Byrne, Director of Artificial Intelligence Research at AVAIL (the Advanced Vanderbilt Artificial Intelligence Laboratory), and author of landmark pragmatic studies published in leading medical journals, shares four decades of experience as a biostatistician and AI researcher. Building on his first book, Publishing Your Medical Research, the author gives the reader the competitive advantage in creating reproducible AI research that will be accepted in prestigious high-impact medical journals. Provides easy-to-understand explanations of the key concepts in using and evaluating AI in medicine. Offers practical, actionable guidance on the mechanics and implementation of AI applications in medicine. Shares career guidance on a successful future in AI in medicine. Teaches the skills to evaluate AI tools and avoid being misled by the hype. For a wide audience of healthcare professionals impacted by Artificial Intelligence in medicine, including physician-scientists, AI developers, entrepreneurs, and healthcare leaders who need to evaluate AI applications designed to improve safety, quality, and value for their institutions. Enrich Your eBook Reading Experience Read directly on your preferred device(s), such as computer, tablet, or smartphone. Easily convert to audiobook, powering your content with natural language text-to-speech. "--Provided by publisher.Artificial IntelligencePrecision MedicineOutcome Assessment, Health CareMEDICAL / Evidence-Based MedicinebisacshMEDICAL / Preventive MedicinebisacshArtificial intelligenceMedical applicationsArtificial IntelligencePrecision MedicineOutcome Assessment, Health CareMEDICAL / Evidence-Based MedicineMEDICAL / Preventive MedicineArtificial intelligenceMedical applications.610.285/63MED112000MED076000bisacshByrne Daniel W1659726DNLM/DLCDLCBOOK9910984621103321Artificial intelligence for improved patient outcomes4332752UNINA04116nam 22005535 450 991030016210332120230201092832.03-319-95252-810.1007/978-3-319-95252-9(CKB)4100000005679210(DE-He213)978-3-319-95252-9(MiAaPQ)EBC5494673(PPN)229919448(EXLCZ)99410000000567921020180816d2018 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierPhotons The History and Mental Models of Light Quanta /by Klaus Hentschel1st ed. 2018.Cham :Springer International Publishing :Imprint: Springer,2018.1 online resource (XIII, 231 p. 38 illus., 11 illus. in color.) 3-319-95251-X Introduction -- Planck’s and Einstein’s pathways to quantization -- Twelve semantic layers of ‘light quantum’ and ‘photon’ -- Early mental models -- Early reception of the light quantum -- Light quanta reflected in textbooks and science teaching -- The ‘light quantum’ as a ‘conceptual blend’ -- Quantum experiments with photons since 1945 -- What is today’s mental model of the photon? -- Summary.This book focuses on the gradual formation of the concept of ‘light quanta’ or ‘photons’, as they have usually been called in English since 1926. The great number of synonyms that have been used by physicists to denote this concept indicates that there are many different mental models of what ‘light quanta’ are: simply finite, ‘quantized packages of energy’ or ‘bullets of light’? ‘Atoms of light’ or ‘molecules of light’? ‘Light corpuscles’ or ‘quantized waves’? Singularities of the field or spatially extended structures able to interfere? ‘Photons’ in G.N. Lewis’s sense, or as defined by QED, i.e. virtual exchange particles transmitting the electromagnetic force? The term ‘light quantum’ made its first appearance in Albert Einstein’s 1905 paper on a “heuristic point of view” to cope with the photoelectric effect and other forms of interaction of light and matter, but the mental model associated with it has a rich history both before and after 1905. Some of its semantic layers go as far back as Newton and Kepler, some are only fully expressed several decades later, while others initially increased in importance then diminished and finally vanished. In conjunction with these various terms, several mental models of light quanta were developed—six of them are explored more closely in this book. It discusses two historiographic approaches to the problem of concept formation: (a) the author’s own model of conceptual development as a series of semantic accretions and (b) Mark Turner’s model of ‘conceptual blending’. Both of these models are shown to be useful and should be explored further. This is the first historiographically sophisticated history of the fully fledged concept and all of its twelve semantic layers. It systematically combines the history of science with the history of terms and a philosophically inspired history of ideas in conjunction with insights from cognitive science.Physics—PhilosophyQuantum theoryScience—HistoryQuantum opticsPhilosophical Foundations of Physics and AstronomyQuantum PhysicsHistory of ScienceQuantum OpticsPhysics—Philosophy.Quantum theory.Science—History.Quantum optics.Philosophical Foundations of Physics and Astronomy.Quantum Physics.History of Science.Quantum Optics.530.01Hentschel Klausauthttp://id.loc.gov/vocabulary/relators/aut969765BOOK9910300162103321Photons2535758UNINA