Artificial Intelligence in Urologic Malignancies
- 1st Edition - November 25, 2024
- Editor: Himanshu Arora
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 5 5 0 4 - 8
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 5 5 0 5 - 5
Artificial Intelligence in Urologic Malignancies describes current artificial intelligence technology, with an emphasis on prostate cancer applications. The book provides guidance… Read more
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Request a sales quoteArtificial Intelligence in Urologic Malignancies describes current artificial intelligence technology, with an emphasis on prostate cancer applications. The book provides guidance on how artificial intelligence can improve therapeutics, how the power of artificial intelligence integrated with current standard therapy and research can enhance decision-making, and proposes future directions on how to integrate artificial intelligence within clinical applications. This is the perfect reference for scientists and researchers interested in the basic translational research opportunities such as drug discovery, pharmacogenetics, and experimental therapeutics, as well as clinicians interested in how AI applications are integrated with applications.
- Provides guidance on AI integration that is expected to become standard in the future
- Places a special emphasis on prostate cancer and the integration of AI to show how to enhance personalized medicine
- Surveys current techniques and standards that can be shared and applied to fields outside cancer
Researchers of prostate cancer, urologists and oncologists. Bioinformatics specialists, software engineers, data scientists
- Title of Book
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Preface
- Chapter 1. Current advancements of machine learning in healthcare
- Abstract
- Introduction
- Applications of artificial intelligence in clinical medicine
- Artificial intelligence in pathology
- Artificial intelligence in basic science research
- Artificial intelligence in healthcare education
- Artificial intelligence in maintenance of routine healthcare
- Artificial intelligence’s role in precision medicine
- Artificial intelligence in healthcare data management
- Conclusion
- References
- Chapter 2. Machine learning and pathology: a historical perspective
- Abstract
- The early beginnings: initial applications and challenges
- The modern era: artificial intelligence driven breakthroughs and developments
- Artificial intelligence based diagnostic approaches in cancer pathology
- Enhanced analysis and predictive capabilities
- Future prospects
- Integration of machine learning into pathology workflow
- Benefits of digital pathology
- Technological advancements
- Comparative studies and regulatory approvals
- Practical benefits of digital pathology systems
- Interpretation of digital pathology images by artificial intelligence models
- Tumor purity evaluation
- Multiplex and multispectral imaging
- Virtual multistains and tissue profiling
- Quality assurance and teleconsultation
- Patient stratification and prognostication
- Artificial intelligence algorithms in diagnostics
- Integrating artificial intelligence with pathology and oncology
- The future of pathology labs
- Workflow optimization
- Adoption of digital pathology and artificial intelligence: challenges and prospects
- Challenges in adoption
- Prospects for successful adoption
- The black box effect
- Scientific examples and current limitations
- Overcoming the barrier
- Scientific examples and strategies for overcoming barriers
- Conclusion
- References
- Chapter 3. Artificial intelligence in personalized medicine: application of genomics to influence therapy decisions
- Abstract
- Introduction
- The intersection of artificial intelligence, genomics, and personalized medicine
- Genomic data in artificial intelligence: types, generation, and management
- Machine learning and genomic data analysis
- Applications in personalized therapeutic decisions
- Predictive models for monotherapies
- Predictive models for combination therapies
- Integration of artificial intelligence in genomic clinical workflow
- Artificial intelligence-driven genomic applications in urologic malignancies
- Challenges and future perspectives
- Conclusion
- References
- Chapter 4. Artificial intelligence in personalized medicine: Using public repositories to understand patterns in relevant datasets
- Abstract
- Introduction
- Artificial intelligence and big data
- The challenge of big data
- Artificial intelligence systems and their capabilities
- Characteristics of healthcare big data
- Precision medicine (personalized medicine)
- The role of public repositories in the interplay of artificial intelligence and big data
- General healthcare datasets
- Image datasets
- Genome datasets
- X-ray datasets
- Scientific research
- Aggregators
- Conclusion
- References
- Chapter 5. Mutational landscape of cancer and how latest technologies can help in simplifying the understanding
- Abstract
- Introduction
- Genetic tools utilized for prostate cancer detection
- Microarray technology in prostate cancer research
- Applications of microarray in prostate cancer
- Next-generation sequencing
- Insights and applications of next-generation sequencing
- Challenges and limitations
- Tumor mutation burden
- Structural variant burden
- Bioinformatic tools utilized for prostate cancer detection
- The Cancer Genome Atlas
- The Gene Expression Omnibus
- cBIO Cancer Genomics Portal
- Machine learning tools utilized for prostate cancer detection
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Handcrafted versus learned features
- Machine learning as a tool for prostate cancer clinical decision making
- References
- Chapter 6. ChatGPT and healthcare—current and future prospects
- Abstract
- Introduction
- Reducing wait times and improving accessibility
- Supporting patient education
- Improving diagnosis and triage
- Telemedicine and automated healthcare
- Understanding ChatGPT: from language model to healthcare assistant
- Future trends and innovations in artificial intelligence and healthcare
- Conclusion
- References
- Chapter 7. Adversarial networks—enhancing current methodology with new models
- Abstract
- Introduction
- Medical record applications
- Imaging classification
- Computed tomography scan enhancement
- Magnetic resonance imaging generative adversarial network integration
- Tissue/pathology classification in cancer
- Enhancing diagnostic imaging
- Boosting early detection and diagnosis
- Augmenting personalized treatment planning
- Research applications
- Drug development
- Pharmacokinetics/pharmacodynamics
- References
- Chapter 8. Limitations of artificial intelligence in healthcare
- Abstract
- Introduction
- Maintenance requires human interaction
- Surveillance
- Culture changes
- Inequality and discrimination
- Cybersecurity risks
- References
- Index
- No. of pages: 350
- Language: English
- Edition: 1
- Published: November 25, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780443155048
- eBook ISBN: 9780443155055
HA
Himanshu Arora
Dr. Arora is Assistant Professor in Department of Urology at Sylvester Cancer Centre, University of Miami. He is the recipient of the Young Investigator Award from Regional Center for Biotechnology, UNESCO, for working on drug discovery in cancer. In 2016 he joined the University of Miami as a research scientist. He has several peer reviewed articles, patents, and grants. In 2019 he was promoted to a faculty position at the University of Miami. Currently, his lab is focusing on exploring therapeutic efficacy of Nitric oxide donors against different stages of Prostate cancer progression, using Nitric oxide donors in combination with currently available immune checkpoint inhibitors, and using machine learning tools to study the progression of prostate cancer and applying this knowledge in building up translational tools that could be used by researchers, clinicians and patients.
Affiliations and expertise
Research Assistant Professor, Assistant Professor of Research of Urology/Oncology (prostate cancer), Sylvester Cancer Centre, University of Miami, USARead Artificial Intelligence in Urologic Malignancies on ScienceDirect