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.
1. Artificial Intelligence for Medicine - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Contributors - p. Page v 7. About the editors - p. Page vi 8. List of Illustrations - p. Page viii 9. List of Tables - p. Page ix 10. Chapter 1 : Artificial intelligence in cancer research and precision medicine - p. Page ix 11. Chapter 2 : Machine learning in computational pathology through self-supervised learning and vision transformers - p. Page 17 12. Chapter 3 : Artificial intelligence in small-molecule drug discovery - p. Page 34 13. Chapter 4 : AI/ML and drug repurposing in lung cancer: State of the art and potential roles for retinoids - p. Page 45 14. Chapter 5 : Artificial intelligence and digital worlds: New frontiers of integration between AI and other technological tools - p. Page 59 15. Chapter 6 : The dual path of the technology acceptance model: An application of machine learning cardiotocography in delivery rooms - p. Page 69 16. Chapter 7 : Artificial intelligence in diagnostic and predictive pathology - p. Page 79 17. Chapter 8 : Artificial intelligence in the oncology workflow: Applications, limitations, and future perspectives - p. Page 88 18. Chapter 9 : SOK: Application of machine learning models in child and youth mental health decision-making - p. Page 107 19. Chapter 10 : Cancer detection in hyperspectral imagery using artificial intelligence: Current trends and future directions - p. Page 129 20. Chapter 11 : Global research trends of Artificial Intelligence and Machine Learning applied in medicine: A bibliometric analysis (2012–2022) - p. Page 147 21. Chapter 12 : Ethics and regulations for AI in radiology - p. Page 175 22. Chapter 13 : The role of artificial intelligence in radiology and interventional oncology - p. Page 190 23. Chapter 14 : The multiomics revolution in the era of deep learning: Allies or enemies? - p. Page 199 24. Chapter 15 : Artificial intelligence in behavioral health economics: Considerations for designing behavioral studies - p. Page 212 25. Chapter 16 : Artificial intelligence and medicine: A psychological perspective on AI implementation in healthcare context - p. Page 229 26. Chapter 17 : AI for outcome prediction in Radiation Oncology: The present and the future - p. Page 235 27. Chapter 18 : Artificial intelligence in neurologic disease - p. Page 250 28. Chapter 19 : Should I trust this model? Explainability and the black box of artificial intelligence in medicine - p. Page 262 29. Index - p. Page 272
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.
1. Artificial Intelligence for Medicine - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Contributors - p. Page v 7. About the editors - p. Page vi 8. List of Illustrations - p. Page viii 9. List of Tables - p. Page ix 10. Chapter 1 : Artificial intelligence in cancer research and precision medicine - p. Page ix 11. Chapter 2 : Machine learning in computational pathology through self-supervised learning and vision transformers - p. Page 17 12. Chapter 3 : Artificial intelligence in small-molecule drug discovery - p. Page 34 13. Chapter 4 : AI/ML and drug repurposing in lung cancer: State of the art and potential roles for retinoids - p. Page 45 14. Chapter 5 : Artificial intelligence and digital worlds: New frontiers of integration between AI and other technological tools - p. Page 59 15. Chapter 6 : The dual path of the technology acceptance model: An application of machine learning cardiotocography in delivery rooms - p. Page 69 16. Chapter 7 : Artificial intelligence in diagnostic and predictive pathology - p. Page 79 17. Chapter 8 : Artificial intelligence in the oncology workflow: Applications, limitations, and future perspectives - p. Page 88 18. Chapter 9 : SOK: Application of machine learning models in child and youth mental health decision-making - p. Page 107 19. Chapter 10 : Cancer detection in hyperspectral imagery using artificial intelligence: Current trends and future directions - p. Page 129 20. Chapter 11 : Global research trends of Artificial Intelligence and Machine Learning applied in medicine: A bibliometric analysis (2012–2022) - p. Page 147 21. Chapter 12 : Ethics and regulations for AI in radiology - p. Page 175 22. Chapter 13 : The role of artificial intelligence in radiology and interventional oncology - p. Page 190 23. Chapter 14 : The multiomics revolution in the era of deep learning: Allies or enemies? - p. Page 199 24. Chapter 15 : Artificial intelligence in behavioral health economics: Considerations for designing behavioral studies - p. Page 212 25. Chapter 16 : Artificial intelligence and medicine: A psychological perspective on AI implementation in healthcare context - p. Page 229 26. Chapter 17 : AI for outcome prediction in Radiation Oncology: The present and the future - p. Page 235 27. Chapter 18 : Artificial intelligence in neurologic disease - p. Page 250 28. Chapter 19 : Should I trust this model? Explainability and the black box of artificial intelligence in medicine - p. Page 262 29. Index - p. Page 272
