Explainable Machine Learning for Multimedia Based Healthcare Applications PDF Download
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Author: M. Shamim Hossain
Publisher: Springer Nature
ISBN: 3031380363
Category :
Languages : en
Pages : 240
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Book Description
Author: M. Shamim Hossain
Publisher: Springer Nature
ISBN: 3031380363
Category :
Languages : en
Pages : 240
Get Book Here
Book Description
Author: Arash Shaban-Nejad
Publisher: Springer Nature
ISBN: 3030533522
Category : Technology & Engineering
Languages : en
Pages : 351
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Book Description
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.
Author: Albuquerque, Victor Hugo C. de
Publisher: IGI Global
ISBN: 1668437929
Category : Computers
Languages : en
Pages : 347
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Book Description
Explainable artificial intelligence is proficient in operating and analyzing the unconstrainted environment in fields like robotic medicine, robotic treatment, and robotic surgery, which rely on computational vision for analyzing complex situations. Explainable artificial intelligence is a well-structured customizable technology that makes it possible to generate promising unbiased outcomes. The model’s adaptability facilitates the management of heterogeneous healthcare data and the visualization of biological structures through virtual reality. Explainable artificial intelligence has newfound applications in the healthcare industry, such as clinical trial matching, continuous healthcare monitoring, probabilistic evolutions, and evidence-based mechanisms. Principles and Methods of Explainable Artificial Intelligence in Healthcare discusses explainable artificial intelligence and its applications in healthcare, providing a broad overview of state-of-the-art approaches for accurate analysis and diagnosis. The book also encompasses computational vision processing techniques that handle complex data like physiological information, electronic healthcare records, and medical imaging data that assist in earlier prediction. Covering topics such as neural networks and disease detection, this reference work is ideal for industry professionals, practitioners, academicians, researchers, scholars, instructors, and students.
Author: Meenu Gupta
Publisher: CRC Press
ISBN: 1040256015
Category : Health & Fitness
Languages : en
Pages : 287
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Book Description
This book provides a thorough exploration of the intersection between gender-based healthcare disparities and the transformative potential of artificial intelligence (AI) and machine learning (ML). It covers a wide range of topics from fundamental concepts to practical applications. Transforming Gender-Based Healthcare with AI and Machine Learning incorporates real-world case studies and success stories to illustrate how AI and ML are actively reshaping gender-based healthcare and offers examples that showcase tangible outcomes and the impact of technology in healthcare settings. The book delves into the ethical considerations surrounding the use of AI and ML in healthcare and addresses issues related to privacy, bias, and responsible technology implementation. Empasis is placed on patient-centered care, and the book discusses how technology empowers individuals to actively participate in their healthcare decisions and promotes a more engaged and informed patient population. Written to encourage interdisciplinary collaboration and highlight the importance of cooperation between health professionals, technologies, researchers, and policymakers, this book portrays how this collaborative approach is essential for achieving transformative goals and is not only for professionals but can also be used at the student level as well.
Author: Lilhore, Umesh Kumar
Publisher: IGI Global
ISBN:
Category : Medical
Languages : en
Pages : 373
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Book Description
The use of artificial intelligence (AI) in data-driven medicine has revolutionized healthcare, presenting practitioners with unprecedented tools for diagnosis and personalized therapy. However, this progress comes with a critical concern: the security and privacy of sensitive patient data. As healthcare increasingly leans on AI, the need for robust solutions to safeguard patient information has become more pressing than ever. Federated Learning and Privacy-Preserving in Healthcare AI emerges as the definitive solution to balancing medical progress with patient data security. This carefully curated volume not only outlines the challenges of federated learning but also provides a roadmap for implementing privacy-preserving AI systems in healthcare. By decentralizing the training of AI models, federated learning mitigates the risks associated with centralizing patient data, ensuring that critical information never leaves its original location. Aimed at healthcare professionals, AI experts, policymakers, and academics, this book not only delves into the technical aspects of federated learning but also fosters a collaborative approach to address the multifaceted challenges at the intersection of healthcare and AI.
Author: JOSEPH BAMIDELE AWOTUNDE
Publisher: CRC Press
ISBN: 1040275397
Category : Computers
Languages : en
Pages : 425
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Book Description
Industry 4.0 and 5.0 applications will revolutionize production, enabling smart manufacturing machines to interact with their environments. These machines will become self-aware, self-learning, and capable of real-time data interpretation for self-diagnosis and prevention of production issues. They will also self-calibrate and prioritize tasks to enhance production quality and efficiency. Computational Intelligence in Industry 4.0 and 5.0 Applications examines applications that merge three key disciplines: computational intelligence (CI), Industry 4.0, and Industry 5.0. It presents solutions using Industrial Internet of Things (IIoT) technologies, augmented by CI-based techniques, modeling, controls, estimations, applications, systems, and future scopes. These applications use data from smart sensors, processed through enhanced CI methods, to make smart automation more effective. Industry 4.0 integrates data and intelligent automation into manufacturing, using technologies like CI, the IoT, the IIoT, and cloud computing. It transforms data into actionable insights for decision-making and process optimization, essential for modern competitive businesses managing high-speed data integration in production processes. Currently, Industries 4.0 and 5.0 are undergoing significant transformations due to advances in applying artificial intelligence (AI), big data analytics, telecommunication technologies, and control theory. These applications are increasingly multidisciplinary, integrating mechanical, control, and information technologies. However, they face such technical challenges as parametric uncertainties, external disturbances, sensor noise, and mechanical failures. To address these, this book examines such CI technologies as fuzzy logic, neural networks, and reinforcement learning and their application to modeling, control, and estimation. It also covers recent advancements in IIoT sensors, microcontrollers, and big data analytics that further enhance CI-based solutions in Industry 4.0 and 5.0 systems.
Author: Abhishek, Kumar
Publisher: IGI Global
ISBN:
Category : Technology & Engineering
Languages : en
Pages : 442
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Book Description
The convergence of Internet of Things (IoT) technology and blockchain offers transformative potential for the development of smart cities, enhancing industry operations and healthcare systems. IoT devices generate vast amounts of data to optimize urban infrastructure and improve service delivery, while blockchain provides a secure, transparent framework for managing data. Across industries, this collaboration leads to smarter manufacturing processes and efficient logistics. In healthcare, it enhances patient care through secure data sharing and streamlined administrative processes. A concerted effort to address these technical, regulatory, and ethical challenges is crucial for effective and responsible integration of IoT and blockchain in smart cities for improved urban living and healthcare services. Applying Internet of Things and Blockchain in Smart Cities: Industry and Healthcare Perspectives explores the application of IoT and blockchain technology for smart city integration in healthcare industries and business processes. It offers solutions for this effective convergence, through aspects like cloud and digital technology, or security and privacy practices. This book covers topics such as machine learning, energy management, and wearable devices, and is a useful resource for business owners, computer engineers, agriculturalists, security professionals, healthcare workers, academicians, researchers, and scientists.
Author: Adam Bohr
Publisher: Academic Press
ISBN: 0128184396
Category : Computers
Languages : en
Pages : 385
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Book Description
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
Author: Youssef Mejdoub
Publisher: Springer Nature
ISBN: 3031704118
Category : Artificial intelligence
Languages : en
Pages : 442
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Book Description
This book aims at meeting the challenge of getting along with today's unprecedented rate of innovation supported by disruptive digital technologies, which changed the perception of the productivity and effectiveness and opened a gateway to more than ever dynamic advances in solving the important societal challenges. "Disruptive Information Technologies for a Smart Society" is the proceedings book of the 14th International Conference for Information Society and Technologies that brings together experts from various fields to discuss the latest advancements in industrial AI, digitalization in health, well-being and sport, enterprise information systems, large language models, and security and safety. The book and the conference serve as a platform for researchers of all career stages in technical sciences, especially Ph.D. students, practitioners, and industry experts in health care, AI and other areas to share and learn on the cutting-edge technologies and stay at the forefront of these rapidly evolving fields.
Author: Wojciech Samek
Publisher: Springer Nature
ISBN: 3030289540
Category : Computers
Languages : en
Pages : 435
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Book Description
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.
