Artificial Intelligence in Urology
Present and Future
- 1st Edition - October 15, 2024
- Imprint: Academic Press
- Editor: Andrew J Hung
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 2 1 3 2 - 3
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 2 1 3 1 - 6
Artificial Intelligence in Urology: Present and Future summarizes the cutting-edge development and adoption of Artificial Intelligence (AI) technologies in urology. The book expl… Read more
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Request a sales quoteArtificial Intelligence in Urology: Present and Future summarizes the cutting-edge development and adoption of Artificial Intelligence (AI) technologies in urology. The book explores barriers that prevent the further adoption of AI technologies, provides ethical considerations, and investigates the future role AI is expected to play. In addition, it includes applications of AI technology in the diagnosis and treatment of cancers (prostate, bladder, and more) and kidney stones, and in both adult and pediatric care. This is the perfect reference for researchers developing AI technologies for clinical applications and for clinicians who aim to effectively adopt AI technologies to solve clinical questions.
- Describes the state-of-the-art of AI applications in urology by leading experts in the field
- Provides a comprehensive review of how different strategies in AI are utilized to solve a wide range of clinical problems—from diagnosis, treatment, and prognostication of urologic diseases
- Comprises contents that can be used as a springboard to allow readers to adopt AI technologies in their field of study/practice
Urologists, both clinicians and clinician-scientists, Data scientists interested in AI application in urology/healthcare, Industry influencers of AI in healthcare
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- List of contributors
- Chapter 1. Introduction
- Abstract
- Chapter 2. What is artificial intelligence, machine learning, and deep learning: terminologies explained
- Abstract
- 2.1 Differences between artificial intelligence/machine learning/deep learning
- 2.2 Machine learning categories
- 2.3 Deep learning and neural networks
- Artificial intelligence disclosure
- References
- Chapter 3. Prostate cancer diagnosis using artificial intelligence methods—radiomics
- Abstract
- 3.1 Introduction
- 3.2 Radiomics: fundamentals and concepts
- 3.3 Prostate imaging modalities
- 3.4 Regulatory considerations
- 3.5 Integration into clinical workflow
- 3.6 Conclusion
- References
- Chapter 4. Advancing prostate cancer diagnosis and treatment through pathomics and artificial intelligence
- Abstract
- 4.1 Introduction
- 4.2 Advanced imaging in prostate cancer
- 4.3 Future implications of advanced imaging
- 4.4 The convergence of advanced imaging in prostate cancer and pathomics
- 4.5 The convergence of artificial intelligence and pathomics
- 4.6 Navigating limitations and moving forward in AI for prostate cancer
- 4.7 Ethical considerations and future directions
- 4.8 Emerging technologies and methodologies in pathomics
- 4.9 AI disclosure
- References
- Chapter 5. Prostate cancer diagnosis using artificial intelligence methods—genomics
- Abstract
- 5.1 Introduction
- 5.2 Artificial intelligence integration with genomic alterations
- 5.3 Artificial intelligence integration with biomarkers
- 5.4 Artificial intelligence integration with histopathology
- 5.5 Artificial intelligence integration with radiogenomics
- 5.6 Conclusion
- References
- Chapter 6. Kidney cancer diagnostics using AI and radiomics
- Abstract
- Abbreviations
- 6.1 Current diagnostics
- 6.2 Conclusion
- References
- Chapter 7. Renal cell carcinoma therapeutics guided by artificial intelligence methods
- Abstract
- 7.1 Introduction
- 7.2 Harnessing artificial intelligence throughout the renal cell carcinoma care continuum
- 7.3 Enhancing perioperative care through artificial intelligence’s applications
- 7.4 Optimizing patient selection for adjuvant therapy
- 7.5 Integrating digital pathology and pathomics in artificial intelligence-driven kidney therapeutics
- 7.6 Conclusion
- References
- Chapter 8. Bladder cancer diagnosis with AI, cystoscopy and pathomics
- Abstract
- 8.1 Introduction
- 8.2 Cystoscopy
- 8.3 AI in cystoscopy
- 8.4 Cytology
- 8.5 AI in cytology
- 8.6 Histopathology
- 8.7 AI in histopathology
- 8.8 Conclusion
- References
- Chapter 9. Bladder cancer treatment with artificial intelligence
- Abstract
- 9.1 Neoadjuvant therapy with artificial intelligence
- 9.2 Robotic surgery in bladder cancer with artificial intelligence
- 9.3 Survival prediction in bladder cancer with artificial intelligence
- 9.4 Recurrence prediction in bladder cancer with artificial intelligence
- 9.5 Prospects of artificial intelligence application in bladder cancer
- References
- Chapter 10. Other genitourinary cancers and AI (penile, urethra, and testes)
- Abstract
- 10.1 Introduction
- 10.2 Testicular cancer
- 10.3 Penile and urethral cancers
- 10.4 Conclusions and future directions
- References
- Chapter 11. Artificial intelligence applications in kidney stone disease
- Abstract
- 11.1 Introduction
- 11.2 Diagnosis and metabolic evaluation
- 11.3 Management
- 11.4 Prevention of stone occurrence/recurrence
- 11.5 Future considerations and conclusion
- References
- Chapter 12. Pediatric urology and AI
- Abstract
- 12.1 Introduction
- 12.2 Diagnosis of pediatric urologic conditions
- 12.3 Patient outcomes
- 12.4 Predicting risk factors
- 12.5 Ethical considerations and future directions
- References
- Chapter 13. Multi-omics in urologic cancers
- Abstract
- 13.1 Introduction
- 13.2 Single-omics and biomarkers in urologic cancers
- 13.3 What is multiomics?
- 13.4 Current multi-omic approaches in urologic cancers
- 13.5 Integration of multi-omics and machine learning
- 13.6 Limitations
- 13.7 The future of multiomics in urologic cancers
- 13.8 Prospective
- 13.9 Conclusions
- References
- Chapter 14. AI in surgery
- Abstract
- 14.1 Introduction
- 14.2 Computer vision
- 14.3 Surgical guidance, training, and assessment
- 14.4 Decision-making
- 14.5 Challenges and limitations
- 14.6 Conclusion
- Acknowledgments
- Artificial intelligence disclosure
- References
- Chapter 15. State-of-art and the future of autonomous surgery
- Abstract
- 15.1 Introduction
- 15.2 The paradigms of modern surgery
- 15.3 The evolution of minimally invasive surgery
- 15.4 The need for autonomous robotic surgery: a new surgical paradigm
- 15.5 Levels of surgical robot autonomy
- 15.6 An introduction to AI methods to achieve full autonomy
- 15.7 Simulation and reinforcement learning-based autonomy
- 15.8 Imitation-based autonomy
- 15.9 The future of autonomous robots
- References
- Chapter 16. Reproductive medicine and AI
- Abstract
- 16.1 Introduction
- 16.2 Artificial intelligence, male infertility, and reproductive medicine: an overview
- 16.3 Artificial intelligence and semen analysis
- 16.4 Artificial intelligence in assisted reproductive technologies
- 16.5 Artificial intelligence and predictive modeling
- 16.6 Ethical considerations
- Conflicts of interest
- References
- Chapter 17. Future directions for AI in urology
- Index
- Edition: 1
- Published: October 15, 2024
- Imprint: Academic Press
- No. of pages: 320
- Language: English
- Paperback ISBN: 9780443221323
- eBook ISBN: 9780443221316
AH
Andrew J Hung
Dr. Andrew J. Hung is an expert in robotic, laparoscopic, and traditional open surgery for diseases of the adrenal, kidney, ureter, bladder, and prostate. He is a recognized leader in the validation and development of innovative surgical simulation technologies. To train the next generation of urologic surgeons, he developed the first-ever procedure-specific simulation for robotic surgery. Supported by both industry and the National Institutes of Health, Dr. Hung has also become a leading innovator in the development of automated performance metrics for robotic surgery. His collaboration with data scientists has harnessed machine learning algorithms to better predict robotic surgical outcomes. Dr. Hung has produced several first-author and senior-author papers on surgical assessment and training in leading journals and is a regular peer-reviewer for leading urologic journals. He currently serves as the first Consulting Editor on Artificial Intelligence for the British Journal of Urology International.
Affiliations and expertise
Associate Professor of Urology & Computational Biomedicine, Vice Chair for Academic Development, Department of Urology, Cedars-Sinai Medical Center, Los Angeles, CaliforniaRead Artificial Intelligence in Urology on ScienceDirect