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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.
Following from the very successful First KES Symposium on Agent and Multi-Agent Systems – Technologies and Applications (KES-AMSTA 2007), held in Wroclaw, Poland, 31 May–1 June 2007, the second event in the KES-AMSTA symposium series (KES-AMSTA 2008) was held in Incheon, Korea, March 26–28, 2008. The symposium was organized by the School of Computer and Information Engineering, Inha University, KES International and the KES Focus Group on Agent and Mul- agent Systems. The KES-AMSTA Symposium Series is a sub-series of the KES Conference Series. The aim of the symposium was to provide an international forum for scientific research into the technologies and applications of agent and multi...
This book explores the ethical governance of Artificial Intelligence (AI) & Machine Learning (ML) in healthcare. AI/ML usage in healthcare as well as our daily lives is not new. However, the direct, and oftentimes long-term effects of current technologies, in addition to the onset of future innovations, have caused much debate about the safety of AI/ML. On the one hand, AI/ML has the potential to provide effective and efficient care to patients, and this sways the argument in favor of continuing to use AI/ML; but on the other hand, the dangers (including unforeseen future consequences of the further development of the technology) leads to vehement disagreement with further AI/ML usage. Due to its potential for beneficial outcomes, the book opts to push for ethical AI/ML to be developed and examines various areas in healthcare, such as big data analytics and clinical decision-making, to uncover and discuss the importance of developing ethical governance for AI/ML in this setting.
This book highlights the latest advances in the application of artificial intelligence to healthcare and medicine. It gathers selected papers presented at the 2019 Health Intelligence workshop, which was jointly held with the Association for the Advancement of Artificial Intelligence (AAAI) annual conference, and presents an overview of the central issues, challenges, and potential opportunities in the field, along with new research results. By addressing a wide range of practical applications, the book makes the emerging topics of digital health and precision medicine accessible to a broad readership. Further, it offers an essential source of information for scientists, researchers, students, industry professionals, national and international public health agencies, and NGOs interested in the theory and practice of digital and precision medicine and health, with an emphasis on risk factors in connection with disease prevention, diagnosis, and intervention.
This book aims to highlight the latest achievements in the use of AI and multimodal artificial intelligence in biomedicine and healthcare. Multimodal AI is a relatively new concept in AI, in which different types of data (e.g. text, image, video, audio, and numerical data) are collected, integrated, and processed through a series of intelligence processing algorithms to improve performance. The edited volume contains selected papers presented at the 2022 Health Intelligence workshop and the associated Data Hackathon/Challenge, co-located with the Thirty-Sixth Association for the Advancement of Artificial Intelligence (AAAI) conference, and presents an overview of the issues, challenges, and potentials in the field, along with new research results. This book provides information for researchers, students, industry professionals, clinicians, and public health agencies interested in the applications of AI and Multimodal AI in public health and medicine.
This book aims to highlight the latest achievements in the use of AI in personalized medicine and healthcare delivery. The edited book contains selected papers presented at the 2023 Health Intelligence workshop, co-located with the Thirty-Seven Association for the Advancement of Artificial Intelligence (AAAI) conference, and presents an overview of the issues, challenges, and potentials in the field, along with new research results. This book provides information for researchers, students, industry professionals, clinicians, and public health agencies interested in the applications of AI in medicine and public health.
Geographic Information System (GIS) is one of the most important tools to help us understand public health and many aspects of our lives. Because of COVID-19, GIS has been brought into the spotlight more than ever before. People and civic leaders worldwide are turning to maps and real-time surveillance data to make sense of what has been happening in the world and to get answers to important questions on every aspect of this pandemic. This book examines the role of GIS in managing and controlling the spread of COVID-19 through 12 global projects and a multidisciplinary approach. It explains the innovative uses of GIS not only limited to data organization and data access, but also how improve...
Vaccines prevent 3 million of deaths every year and are a crucial to combat antimicrobial resistance. An optimal implementation of existing vaccines could help to avert up to 1.5 million deaths as well as substantial disabilities. Indeed, vulnerable populations (VPs), including pregnant women, newborns, preterm infants, elderly and patients affected by chronic diseases are frequently undervaccinated and/or at risk of reduced vaccine efficacy, presenting a major health and economic burden to society. Vaccine development programs mainly focus on healthy populations; therefore, vaccination strategies are often based on data arising from healthy subjects.
This book constitutes the refereed proceedings of the 13th Conference on Artificial Intelligence in Medicine, AIME 2011, held in Bled, Slovenia, in July 2011. The 42 revised full and short papers presented together with 2 invited talks were carefully reviewed and selected from 113 submissions. The papers are organized in topical sections on knowledge-based systems; data mining; special session on AI applications; probabilistic modeling and reasoning; terminologies and ontologies; temporal reasoning and temporal data mining; therapy planning, scheduling and guideline-based care; and natural language processing.