Artificial Intelligence for Medicine

An Applied Reference for Methods and Applications

Shai Ben- David, Giuseppe Curigliano, David Koff, Barbara Alicja Jereczek-Fossa, Davide La Torre, Gabriella Pravettoni, Shai Ben- David, Giuseppe Curigliano, David Koff, Barbara Alicja Jereczek-Fossa, Davide La Torre, Gabriella Pravettoni

• Publisher: Academic Press
• Year: 2024
• Print ISBN:
  9780443136719
• eBook ISBN:
  9780443136726
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

Artificial Intelligence for Medicine: An Applied Reference for Methods and Applications introduces readers to the methodology and AI/ML algorithms as well as cutting-edge applications to medicine, such as cancer, precision medicine, critical care, personalized medicine, telemedicine, drug discovery, molecular characterization, and patient mental health. Research in medicine and tailored clinical treatment are being quickly transformed by artificial intelligence (AI) and machine learning (ML). The content in this book is tailored to the reader's needs in terms of both type and fundamentals. It covers the current ethical issues and potential developments in this field. Artificial Intelligence for Medicine is beneficial for academics, professionals in the IT industry, educators, students, and anyone else involved in the use and development of AI in the medical field. - Covers the basic concepts of Artificial Intelligence and Machine Learning, methods and practices, and advanced topics and applications to clinical and precision medicine - Presents readers with an understanding of how AI is revolutionizing medicine by demonstrating the applications of computational intelligence to the field, along with an awareness of how AI can improve upon traditional medical structures - Provides researchers, practitioners, and project stakeholders with a complete guide for applying AI techniques in their projects and solutions

Table of contents

• Cover
• Front Matter
• Table of Contents
• Copyright
• Contributors
• About the editors
• List of Illustrations
• List of Tables
• Chapter 1 : Artificial intelligence in cancer research and precision medicine
• Chapter 2 : Machine learning in computational pathology through self-supervised learning and vision transformers
• Chapter 3 : Artificial intelligence in small-molecule drug discovery
• Chapter 4 : AI/ML and drug repurposing in lung cancer: State of the art and potential roles for retinoids
• Chapter 5 : Artificial intelligence and digital worlds: New frontiers of integration between AI and other technological tools
• Chapter 6 : The dual path of the technology acceptance model: An application of machine learning cardiotocography in delivery rooms
• Chapter 7 : Artificial intelligence in diagnostic and predictive pathology
• Chapter 8 : Artificial intelligence in the oncology workflow: Applications, limitations, and future perspectives
• Chapter 9 : SOK: Application of machine learning models in child and youth mental health decision-making
• Chapter 10 : Cancer detection in hyperspectral imagery using artificial intelligence: Current trends and future directions
• Chapter 11 : Global research trends of Artificial Intelligence and Machine Learning applied in medicine: A bibliometric analysis (2012–2022)
• Chapter 12 : Ethics and regulations for AI in radiology
• Chapter 13 : The role of artificial intelligence in radiology and interventional oncology
• Chapter 14 : The multiomics revolution in the era of deep learning: Allies or enemies?
• Chapter 15 : Artificial intelligence in behavioral health economics: Considerations for designing behavioral studies
• Chapter 16 : Artificial intelligence and medicine: A psychological perspective on AI implementation in healthcare context
• Chapter 17 : AI for outcome prediction in Radiation Oncology: The present and the future
• Chapter 18 : Artificial intelligence in neurologic disease
• Chapter 19 : Should I trust this model? Explainability and the black box of artificial intelligence in medicine
• Index
• A