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The twenty chapters in this book offer a comprehensive overview of the theory, algorithms, and applications of interpretable and Explainable Artificial Intelligence (XAI). They cover recent advances in the field of healthcare, reflect the current discourse, and offer suggestions for future research. The book is divided into four sections: primitive concepts of XAI, XAI in smart tele-medicine and tele-health, public health application using XAI, and medical imaging classification using XAI. Thus, the book covers a comprehensive set of material ranging from fundamentals to image analysis employing XAI ideas.
Explainable Artificial Intelligence (XAI) is a next-generation research field that aims to make AI and deep models more human-interpretable without compromising performance. Healthcare and its services are built around trust and high ethical standards to provide quality of life through automated tasks to produce more accurate diagnoses and treatment plans it helps healthcare be more predictive and proactive by analyzing big data to develop improved patient preventive care recommendations. The methods should be more promising in adaptability and immunity against the observatory variables with cost-effective solutions. The book aims to encourage and invite the researcher with novel ideas and multi-disciplinary transformers to introduce the practical approach for analyzing various healthcare data to exhibit the unique challenges in the healthcare domain. This edited book covers 360-degree views of the development and automation of the healthcare domain using XAI to analyze smart healthcare systems. The wide variety of topics presented in this book offers the readers multiple perspectives on various disciplines.
Artificial Intelligence (AI), Machine Learning, and Deep Learning models have paved a solid predictive path in numerous real-life applications, including medical imaging and healthcare tasks. In healthcare, decisions and investigation process is taken with great care, but risks are associated with diagnosing a disease. The diagnosis of the patient's illness is analyzed based on various symptoms, observations, test reports, and experience. However, there is a chance of uncertainty while analyzing the data. Additionally, a second opinion is sought in many cases. Using AI, it is possible to automate the model to mimic the process of a domain expert so that a physician can think of alternative decisions while making decisions. Specifically, XAI provides a clear picture of prediction concerning Deep Learning models and the use of predictedresults in healthcare applications. This book aims to provide the basic and intermediate notions of XAI and its application to healthcare applications.
The book includes four sections comprising primitive concepts under XAI, XAI in smart telemedicine and telehealth, public health applications using XAI, and medical imaging classification using XAI. The first five chapters comprise section 1, followed by four chapters in section 2. Six chapters further follow it in section 3. The final section, 4, includes five chapters.