Artificial Intelligence for Medicine: An Applied Reference for Methods and Applications: Advanced Studies in Complex Systems
Editat de Shai Ben- David, Giuseppe Curigliano, David Koff, Barbara Alicja Jereczek-Fossa, Davide La Torre, Gabriella Pravettonien Limba Engleză Paperback – 18 mar 2024
This book will be 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
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Specificații
ISBN-13: 9780443136719
ISBN-10: 0443136718
Pagini: 294
Dimensiuni: 216 x 276 x 18 mm
Greutate: 0.69 kg
Editura: ELSEVIER SCIENCE
Seria Advanced Studies in Complex Systems
ISBN-10: 0443136718
Pagini: 294
Dimensiuni: 216 x 276 x 18 mm
Greutate: 0.69 kg
Editura: ELSEVIER SCIENCE
Seria Advanced Studies in Complex Systems
Cuprins
1. Artificial intelligence in cancer research and precision medicine
2. Machine learning in computational pathology through self-supervised learning and vision transformers
3. Artificial intelligence in small-molecule drug delivery
4. AI/ML and drug repurposing in lung cancer: State of the art and potential roles for retinoids
5. Artificial intelligence and digital worlds: New frontiers of integration between AI and other technological tools
6. The dual path of the technology acceptance model: An application of machine learning cardiotocography in delivery rooms
7. Artificial intelligence in diagnostic and predictive pathology
8. Artificial intelligence in the oncology workflow: Applications, limitations, and future perspectives
9. SOK: Application of machine learning models in child and youth mental health decision-making
10. Cancer detection in hyperspectral imagery using artificial intelligence: Current trends and future directions
11. Global research trends of Artificial Intelligence and Machine Learning applied in medicine: A bibliometric analysis (2012–2022)
12. Ethics and regulations for AI in radiology
13. The role of artificial intelligence in radiology and interventional oncology
14. The multiomics revolution in the era of deep learning: Allies or enemies?
15. Artificial intelligence in behavioral health economics: Considerations for designing behavioral studies
16. Artificial intelligence and medicine: A psychological perspective on AI implementation in healthcare context
17. AI for outcome prediction in Radiation Oncology: The present and the future
18. Artificial intelligence in neurologic disease
19. Should I trust this model? Explainability and the black box of artificial intelligence in medicine
2. Machine learning in computational pathology through self-supervised learning and vision transformers
3. Artificial intelligence in small-molecule drug delivery
4. AI/ML and drug repurposing in lung cancer: State of the art and potential roles for retinoids
5. Artificial intelligence and digital worlds: New frontiers of integration between AI and other technological tools
6. The dual path of the technology acceptance model: An application of machine learning cardiotocography in delivery rooms
7. Artificial intelligence in diagnostic and predictive pathology
8. Artificial intelligence in the oncology workflow: Applications, limitations, and future perspectives
9. SOK: Application of machine learning models in child and youth mental health decision-making
10. Cancer detection in hyperspectral imagery using artificial intelligence: Current trends and future directions
11. Global research trends of Artificial Intelligence and Machine Learning applied in medicine: A bibliometric analysis (2012–2022)
12. Ethics and regulations for AI in radiology
13. The role of artificial intelligence in radiology and interventional oncology
14. The multiomics revolution in the era of deep learning: Allies or enemies?
15. Artificial intelligence in behavioral health economics: Considerations for designing behavioral studies
16. Artificial intelligence and medicine: A psychological perspective on AI implementation in healthcare context
17. AI for outcome prediction in Radiation Oncology: The present and the future
18. Artificial intelligence in neurologic disease
19. Should I trust this model? Explainability and the black box of artificial intelligence in medicine