-PDF Download- Artificial Intelligence In Medicine Knowledge Representation And Transparent And Explainable Systems EBOOK

Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems

Author: Mar Marcos

Publisher: Springer Nature

Published: 2020-01-03

Total Pages: 184

ISBN-13: 3030374467

This book constitutes revised selected papers from the AIME 2019 workshops KR4HC/ProHealth 2019, the Workshop on Knowledge Representation for Health Care and Process-Oriented Information Systems in Health Care, and TEAAM 2019, the Workshop on Transparent, Explainable and Affective AI in Medical Systems. The volume contains 5 full papers from KR4HC/ProHealth, which were selected out of 13 submissions. For TEAAM 8 papers out of 10 submissions were accepted for publication.

Artificial Intelligence in Medicine

Author: Mar Marcos

Publisher:

Published: 2019

Total Pages: 175

ISBN-13: 9783030374471

DOWNLOAD EBOOK →

Explainable AI in Healthcare and Medicine

Author: Arash Shaban-Nejad

Publisher: Springer Nature

Published: 2020-11-02

Total Pages: 344

ISBN-13: 3030533522

DOWNLOAD EBOOK →

This book highlights the latest advances in the application of artificial intelligence and data science in health care and medicine. Featuring selected papers from the 2020 Health Intelligence Workshop, held as part of the Association for the Advancement of Artificial Intelligence (AAAI) Annual Conference, it offers an overview of the issues, challenges, and opportunities in the field, along with the latest research findings. Discussing a wide range of practical applications, it makes the emerging topics of digital health and explainable AI in health care and medicine accessible to a broad readership. The availability of explainable and interpretable models is a first step toward building a culture of transparency and accountability in health care. As such, this book provides information for scientists, researchers, students, industry professionals, public health agencies, and NGOs interested in the theory and practice of computational models of public and personalized health intelligence.

Artificial Intelligence in Medicine

Author: David Riaño

Publisher: Springer

Published: 2019-06-19

Total Pages: 431

ISBN-13: 303021642X

DOWNLOAD EBOOK →

This book constitutes the refereed proceedings of the 17th Conference on Artificial Intelligence in Medicine, AIME 2019, held in Poznan, Poland, in June 2019. The 22 revised full and 31 short papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in the following topical sections: deep learning; simulation; knowledge representation; probabilistic models; behavior monitoring; clustering, natural language processing, and decision support; feature selection; image processing; general machine learning; and unsupervised learning.

Explainable AI in Healthcare

Author: Mehul S Raval

Publisher: CRC Press

Published: 2023-07-17

Total Pages: 346

ISBN-13: 100090640X

DOWNLOAD EBOOK →

This book combines technology and the medical domain. It covers advances in computer vision (CV) and machine learning (ML) that facilitate automation in diagnostics and therapeutic and preventive health care. The special focus on eXplainable Artificial Intelligence (XAI) uncovers the black box of ML and bridges the semantic gap between the technologists and the medical fraternity. Explainable AI in Healthcare: Unboxing Machine Learning for Biomedicine intends to be a premier reference for practitioners, researchers, and students at basic, intermediary levels and expert levels in computer science, electronics and communications, information technology, instrumentation and control, and electrical engineering. This book will benefit readers in the following ways: Explores state of art in computer vision and deep learning in tandem to develop autonomous or semi-autonomous algorithms for diagnosis in health care Investigates bridges between computer scientists and physicians being built with XAI Focuses on how data analysis provides the rationale to deal with the challenges of healthcare and making decision-making more transparent Initiates discussions on human-AI relationships in health care Unites learning for privacy preservation in health care

Responsible and Explainable Artificial Intelligence in Healthcare

Author: Akansha Singh

Publisher: Elsevier

Published: 2024-12-01

Total Pages: 0

ISBN-13: 0443247897

DOWNLOAD EBOOK →

Responsible and Explainable Artificial Intelligence in Healthcare: Ethics and Transparency at the Intersection provides clear guidance on building trustworthy Artificial Intelligence systems for healthcare. The book focuses on using Artificial Intelligence to improve diagnosis, prevent diseases, and personalize patient care. It addresses potential drawbacks, like reduced human interaction and ethical concerns, offering solutions for ethical and transparent Artificial Intelligence use in medicine. Across eight chapters, the book explores Artificial Intelligence's current status, its importance, and associated risks in healthcare. It explains designing reliable Artificial Intelligence for healthcare, tackling biases, and safeguarding patient privacy in the age of big data. The legal and regulatory landscape is also covered. One chapter is dedicated to showcasing real-world examples of responsible Artificial Intelligence in healthcare, highlighting best practices. The book concludes by summarizing key takeaways and discussing future challenges. "Responsible and Explainable Artificial Intelligence in Healthcare: Ethics and Transparency at the Intersection" is a valuable resource for healthcare professionals, policymakers, computer scientists, and ethicists concerned about Artificial Intelligence's ethical and societal impact on medicine. Gives insights into the responsible and explainable use of Artificial Intelligence in healthcare and explore the challenges and opportunities for promoting ethical and transparent practices in this field Offers the solution to strike a balance between patient privacy and data exchange Provides concrete advice on how to create trustworthy, accountable, and transparent Artificial Intelligence systems Explains the moral and social effects of Artificial intelligence in healthcare and suggests ways to encourage its ethical application

Explainable Artificial Intelligence (XAI) in Healthcare

Author: Utku Kose

Publisher: CRC Press

Published: 2024-04-23

Total Pages: 251

ISBN-13: 1040020453

DOWNLOAD EBOOK →

This book highlights the use of explainable artificial intelligence (XAI) for healthcare problems, in order to improve trustworthiness, performance and sustainability levels in the context of applications. Explainable Artificial Intelligence (XAI) in Healthcare adopts the understanding that AI solutions should not only have high accuracy performance, but also be transparent, understandable and reliable from the end user's perspective. The book discusses the techniques, frameworks, and tools to effectively implement XAI methodologies in critical problems of healthcare field. The authors offer different types of solutions, evaluation methods and metrics for XAI and reveal how the concept of explainability finds a response in target problem coverage. The authors examine the use of XAI in disease diagnosis, medical imaging, health tourism, precision medicine and even drug discovery. They also point out the importance of user perspectives and value of the data used in target problems. Finally, the authors also ensure a well-defined future perspective for advancing XAI in terms of healthcare. This book will offer great benefits to students at the undergraduate and graduate levels and researchers. The book will also be useful for industry professionals and clinicians who perform critical decision-making tasks.

Interactive Process Mining in Healthcare

Author: Carlos Fernandez-Llatas

Publisher: Springer Nature

Published: 2020-10-28

Total Pages: 310

ISBN-13: 3030539938

DOWNLOAD EBOOK →

This book provides a practically applicable guide to the methodologies and technologies for the application of interactive process mining paradigm. Case studies are presented where this paradigm has been successfully applied in emergency medicine, surgery processes, human behavior modelling, strokes and outpatients’ services, enabling the reader to develop a deep understanding of how to apply process mining technologies in healthcare to support them in inferring new knowledge from past actions, and providing accurate and personalized knowledge to improve their future clinical decision-making. Interactive Process Mining in Healthcare comprehensively covers how machine learning algorithms can be utilized to create real scientific evidence to improve daily healthcare protocols, and is a valuable resource for a variety of health professionals seeking to develop new methods to improve their clinical decision-making.

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

Author: Wojciech Samek

Publisher: Springer Nature

Published: 2019-09-10

Total Pages: 435

ISBN-13: 3030289540

DOWNLOAD EBOOK →

The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.

Intelligent Systems in Medicine and Health

Author: Trevor A. Cohen

Publisher: Springer Nature

Published: 2022-11-09

Total Pages: 607

ISBN-13: 3031091086

DOWNLOAD EBOOK →

This textbook comprehensively covers the latest state-of-the-art methods and applications of artificial intelligence (AI) in medicine, placing these developments into a historical context. Factors that assist or hinder a particular technique to improve patient care from a cognitive informatics perspective are identified and relevant methods and clinical applications in areas including translational bioinformatics and precision medicine are discussed. This approach enables the reader to attain an accurate understanding of the strengths and limitations of these emerging technologies and how they relate to the approaches and systems that preceded them. With topics covered including knowledge-based systems, clinical cognition, machine learning and natural language processing, Intelligent Systems in Medicine and Health: The Role of AI details a range of the latest AI tools and technologies within medicine. Suggested additional readings and review questions reinforce the key points covered and ensure readers can further develop their knowledge. This makes it an indispensable resource for all those seeking up-to-date information on the topic of AI in medicine, and one that provides a sound basis for the development of graduate and undergraduate course materials.