Trustworthy Machine Learning For Healthcare
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Trustworthy Machine Learning For Healthcare
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Author : Hao Chen
language : en
Publisher: Springer Nature
Release Date : 2023-07-30
Trustworthy Machine Learning For Healthcare written by Hao Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-30 with Computers categories.
This book constitutes the proceedings of First International Workshop, TML4H 2023, held virtually, in May 2023. The 16 full papers included in this volume were carefully reviewed and selected from 30 submissions. The goal of this workshop is to bring together experts from academia, clinic, and industry with an insightful vision of promoting trustworthy machine learning in healthcare in terms of scalability, accountability, and explainability.
Practicing Trustworthy Machine Learning
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Author : Yada Pruksachatkun
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-01-03
Practicing Trustworthy Machine Learning written by Yada Pruksachatkun and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-03 with Business & Economics categories.
With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. Authors Yada Pruksachatkun, Matthew McAteer, and Subhabrata Majumdar translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world. You'll learn: Methods to explain ML models and their outputs to stakeholders How to recognize and fix fairness concerns and privacy leaks in an ML pipeline How to develop ML systems that are robust and secure against malicious attacks Important systemic considerations, like how to manage trust debt and which ML obstacles require human intervention
Trustworthy Artificial Intelligence For Healthcare
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Author : Hao Chen
language : en
Publisher: Springer Nature
Release Date : 2024-08-01
Trustworthy Artificial Intelligence For Healthcare written by Hao Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-01 with Mathematics categories.
This book constitutes the proceedings of Second International Workshop on Trustworthy Artificial Intelligence for Healthcare, TAI4H 2024, held in Jeju, South Korea, in August 2024, in conjunction with the International Joint Conference on Artificial Intelligence, IJCAI 2024. The 13 full papers included in this book were carefully reviewed and selected from 21 submissions. They focus on trustworthy artificial intelligence, healthcare, generalization, explainability, fairness, privacy, multi-modal fusion, foundation models.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Trustworthy Ai In Medical Imaging
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Author : Marco Lorenzi
language : en
Publisher: Elsevier
Release Date : 2024-11-25
Trustworthy Ai In Medical Imaging written by Marco Lorenzi and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-25 with Computers categories.
Trustworthy AI in Medical Imaging brings together scientific researchers, medical experts, and industry partners working in the field of trustworthiness, bridging the gap between AI research and concrete medical applications and making it a learning resource for undergraduates, masters students, and researchers in AI for medical imaging applications. The book will help readers acquire the basic notions of AI trustworthiness and understand its concrete application in medical imaging, identify pain points and solutions to enhance trustworthiness in medical imaging applications, understand current limitations and perspectives of trustworthy AI in medical imaging, and identify novel research directions. Although the problem of trustworthiness in AI is actively researched in different disciplines, the adoption and implementation of trustworthy AI principles in real-world scenarios is still at its infancy. This is particularly true in medical imaging where guidelines and standards for trustworthiness are critical for the successful deployment in clinical practice. After setting out the technical and clinical challenges of AI trustworthiness, the book gives a concise overview of the basic concepts before presenting state-of-the-art methods for solving these challenges. - Introduces the key concepts of trustworthiness in AI. - Presents state-of-the-art methodologies for trustworthy AI in medical imaging. - Outlines major initiatives focusing on real-world deployment of trustworthy principles in medical imaging applications. - Presents outstanding questions still to be solved and discusses future research directions.
Healthcare Solutions Using Machine Learning
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Author : Dr. Sushil Dohare
language : en
Publisher: Xoffencerpublication
Release Date : 2023-04-24
Healthcare Solutions Using Machine Learning written by Dr. Sushil Dohare and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-24 with Business & Economics categories.
The Turing Test is an experiment that examines whether or not the behaviours of a machine are indistinguishable from those of a human being. The test was named after Alan Turing. It was intended as a test to determine whether or not a computer have the ability to demonstrate "artificial" intelligence. It is inaccurate, and there should be a clear distinction between the two terms. In point of fact, artificial intelligence comprises a variety of learning processes and is not limited to only machine learning alone. Rather, it is about learning in general. Components of artificial intelligence include things like natural language processing, deep learning, and representation learning (NLP). The process of digitalizing, which is also known as "datafication," each and every aspect of life in the present day is referred to as "datafication." The generation of these new data sets paves the way for the transformation of previously collected information into innovative and possibly lucrative forms. Samuel's software was executed on an IBM 701 computer, which was about the same size as a standard double bed. The majority of the time, the data was in discrete form. This is not a reference to the process of really gaining information; rather, it is a reference to the job that is now being carried out. During this stage, a prototype is built by evaluating multiple models in light of historical data to determine which model will be the most successful. Adjusting the model's hyperparameters is a necessary step that will be discussed in further depth in the following section of this chapter. The ideas that determine what constitutes appropriate and inappropriate behaviour are collectively referred to as morality. The subsequent secondary components that need to be looked at are the cost-effectiveness, the quality of the patient experience, and the overall quality of the healthcare provided. The overall number of patients that a provider treats and the total cost of care that patient receives from that provider both go into the financial rewards that the provider receives. The case studies that are presented here provide insightful and thought-provoking insights on the application of artificial intelligence, machine learning, and big data in the field of medicine.
Uncertainty For Safe Utilization Of Machine Learning In Medical Imaging
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Author : Carole H. Sudre
language : en
Publisher: Springer Nature
Release Date : 2024-10-02
Uncertainty For Safe Utilization Of Machine Learning In Medical Imaging written by Carole H. Sudre and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-02 with Computers categories.
This book constitutes the refereed proceedings of the 6th Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, on October 10, 2024. The 20 full papers presented in this book were carefully reviewed and selected from 28 submissions. They are organized in the following topical sections: annotation uncertainty; clinical implementation of uncertainty modelling and risk management in clinical pipelines; out of distribution and domain shift identification and management; uncertainty modelling and estimation.
Artificial Intelligence Based System Models In Healthcare
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Author : A. Jose Anand
language : en
Publisher: John Wiley & Sons
Release Date : 2024-10-01
Artificial Intelligence Based System Models In Healthcare written by A. Jose Anand and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-01 with Computers categories.
Artificial Intelligence-Based System Models in Healthcare provides a comprehensive and insightful guide to the transformative applications of AI in the healthcare system. This book is a groundbreaking exploration of the synergies between artificial intelligence and healthcare innovation. In an era where technological advancements are reshaping the landscape of medical practices, this book provides a comprehensive and insightful guide to the transformative applications of AI in healthcare systems. From conceptual foundations to practical implementations, the book serves as a roadmap for understanding the intricate relationships between AI-based system models and the evolution of healthcare delivery. The first section delves into the fundamental role of technology in reshaping the healthcare landscape. With a focus on daily life activities, decision support systems, vision-based management, and semantic frameworks, this section lays the groundwork for understanding the pivotal role of AI in revolutionizing traditional healthcare approaches. Each chapter offers a unique perspective, emphasizing the intricate integration of technology into healthcare ecosystems. The second section takes a deep dive into specific applications of AI, ranging from predictive analysis and machine learning to deep learning, image analysis, and biomedical text processing. With a focus on decision-making support systems, this section aims to demystify the complex world of AI algorithms in healthcare, offering valuable insights into their practical implications and potential impact on patient outcomes. The final section addresses the modernization of healthcare practices and envisions the future landscape of AI applications. From medical imaging and diagnostics to predicting ventilation needs in intensive care units, modernizing health record maintenance, natural language processing, chatbots for medical inquiries, secured health insurance management, and glimpses into the future, the book concludes by exploring the frontiers of AI-driven healthcare innovations. Audience This book is intended for researchers and postgraduate students in artificial intelligence and the biomedical and healthcare sectors. Medical administrators, policymakers and regulatory specialists will also have an interest.
Advanced Computing
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Author : Deepak Garg
language : en
Publisher: Springer Nature
Release Date : 2024-03-25
Advanced Computing written by Deepak Garg and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-25 with Computers categories.
The two-volume set CCIS 2053 and 2054 constitutes the refereed post-conference proceedings of the 13th International Advanced Computing Conference, IACC 2023, held in Kolhapur, India, during December 15–16, 2023. The 66 full papers and 6 short papers presented in these proceedings were carefully reviewed and selected from 425 submissions. The papers are organized in the following topical sections: Volume I: The AI renaissance: a new era of human-machine collaboration; application of recurrent neural network in natural language processing, AI content detection and time series data analysis; unveiling the next frontier of AI advancement. Volume II: Agricultural resilience and disaster management for sustainable harvest; disease and abnormalities detection using ML and IOT; application of deep learning in healthcare; cancer detection using AI.
Trustworthy Artificial Intelligence In Industry And Society
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Author : Dimple Patil
language : en
Publisher: Deep Science Publishing
Release Date : 2024-10-17
Trustworthy Artificial Intelligence In Industry And Society written by Dimple Patil and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-17 with Computers categories.
Artificial Intelligence (AI) is evolving at an unprecedented rate, changing industries and reshaping social landscapes. However, the question still stands: how can we make sure that, even with this growth, AI stays ethical and trustworthy? In an effort to investigate this issue, the book Trustworthy Artificial Intelligence in Industry and Society provides a thorough analysis of AI's potential to promote resilience, accountability, and trust in a variety of contexts. Chapter 1 explores the essential need for transparent and interpretable AI systems, starting with the foundation of Explainable Artificial Intelligence (XAI) and laying the framework for fostering trust among users, stakeholders, and society at large. In Chapter 2, deep learning and machine learning are explored, along with their applications, methods, and implementation challenges. In Chapter 3, the book delves into the impact of artificial intelligence (AI) on Environmental, Social, and Governance (ESG) initiatives. It specifically highlights the applications of AI in the financial services and investment sectors. We look at the adoption and application of AI in the construction sector in Chapter 4, offering some insight into the drivers, patterns, and obstacles that will shape the technology's future. The use of AI to improve supply chain sustainability and revolutionize the transportation industry is covered in Chapters 5 and 6, with a focus on generative AI technologies and ethical issues. Chapter 7 explores how artificial intelligence is affecting customer relationship management, highlighting how sentiment analysis is transforming customer loyalty and experience. This book seeks to shed light on the opportunities and difficulties that artificial intelligence (AI) brings to business and society by exploring these areas.