Jose Antonio Marmolejo Saucedo – författare

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.

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.

This book presents the proceedings of The EAI International Conference on Computer Science: Applications in Engineering and Health Services (COMPSE 2019). The conference highlighted the latest research innovations and applications of algorithms designed for optimization applications within the fields of Science, Computer Science, Engineering, Information Technology, Management, Finance and Economics and Health Systems. Focusing on a variety of methods and systems as well as practical examples, this conference is a significant resource for post graduate-level students, decision makers, and researchers in both public and private sectors who are seeking research-based methods for modelling uncertain and unpredictable real-world problems.

The 16 full papers presented were carefully reviewed and selected from 39 submissions. The papers highlight the latest research innovations and applications of algorithms designed for optimization applications within the fields of science, computer science, engineering, information technology, economics, and health systems.

The 17 full papers presented were carefully reviewed and selected from 46 submissions. The papers are grouped on thematic topics: application of tools delivered by the COVID-19 pandemic; health services; computer and data science; and industry 4.0 in logistics and supply chain.

This book of Springer Nature is another proof of Springer’s outstanding and greatness on the lively interface of Smart Computational Optimization, Green ICT, Smart Intelligence and Machine Learning! It is a Master Piece of what our community of academics and experts can provide when an Interconnected Approach of Joint, Mutual and Meta Learning is supported by Modern Operational Research and Experience of the World-Leader Springer Nature!

The 5th edition of International Conference on Intelligent Computing and Optimization took place at October 27–28, 2022, via Zoom. Objective was to celebrate “Creativity with Compassion and Wisdom” with researchers, scholars, experts and investigators in Intelligent Computing and Optimization across the planet, to share knowledge, experience, innovation—a marvelous opportunity for discourse and mutuality by novel research, invention and creativity.

This proceedings book of ICO’2022 is published by Springer Nature—Quality Label of wonderful.

This book constitutes the refereed post-conference proceedings of the 6th EAI International Conference Computer on Science and Engineering in Health Services (COMPSE 2022), which took place in Mexico City and online, June 28th, 2022. The papers are grouped on thematic topics: application of tools delivered by the COVID-19 pandemic; health services; computer and data science; and industry 4.0 in logistics and supply chain. The content is relevant to researchers, academics, students and professionals.

This book constitutes the refereed post-conference proceedings of the 7th EAI International Conference on Computer Science and Engineering in Health Services (COMPSE 2023), which took place November 16-17, 2023, in Mexico City, Mexico. The full papers presented were carefully reviewed and selected from dozens of submissions. The papers are grouped on thematic topics: application of tools delivered by the COVID-19 pandemic; health services; computer and data science; and industry 4.0 in logistics and supply chain. The content is relevant to researchers, academics, students and professionals.