Artificial Intelligence for Improved Patient Outcomes: Principles for Moving Forward with Rigorous Science, 1E (1版)

類似書籍推薦給您

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.

FORWARD AND INVERSE SCATTERING ALGORITHMS BASED ON CONTRAST SOURCE INTEGRAL EQUATIONS

類似書籍推薦給您

【簡介】 A guide to wave-field computational methods based on contrast source type of integral equations Forward and Inverse Scattering Algorithms Based on Contrast Source Integral Equations presents a text that examines wave-field computational methods based on contrast source type of integral equations and the computational implementation in wave-field based imaging methods. Written by a noted expert on the topic, the book provides a guide to efficient methods for calculating wave fields in a known inhomogeneous medium. The author provides a link between the fundamental scattering theory and its discrete counterpart and discusses the forward scattering problem based on the contrast-source integral equations. The book fully describes the calculation of wave fields inside and outside a scattering object with general shape and material property and reviews the inverse scattering problem, in which material properties are resolved from wave-field measurements outside the scattering object. The theoretical approach is the inverse of the forward scattering problem that determines how radiation is scattered, based on the scattering object. This important book: Provides a guide to the effects of scalar waves, acoustic waves and electromagnetic waves Describes computer modeling in 1D, 2D and 3D models Includes an online site for computer codes with adjustable configurations Written for students, researchers, and professionals, Forward and Inverse Scattering Algorithms Based on Contrast Source Integral Equations offers a guide to wave-field computational methods based on contrast source type of integral equations and the computational implementation in wave-field based imaging methods. 【目錄】