Artificial Intelligence in Healthcare 1st Edition by Adam Bohr Kaveh Memarzadeh 0128184394 9780128184394

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ISBN-10 :  0128184394 

ISBN-13 : 9780128184394

Author: Adam Bohr Kaveh Memarzadeh 

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges　and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare.

Artificial Intelligence in Healthcare 1st Table of contents:

1 Current healthcare, big data, and machine learning
1.1 Current healthcare practice
1.1.1 The rising need for technology
1.1.2 New models in healthcare
1.2 Value-based treatments and healthcare services
1.2.1 Value-based healthcare
1.2.2 Increasing health outcomes
1.2.3 Patient-centered care (the patient will see you now)
1.2.4 Personalized medicine
1.3 Increasing data volumes in healthcare
1.3.1 Big data and data accumulation
1.3.2 Data generation sources
1.3.3 Big data types
1.4 Analytics of healthcare data (machine learning and deep learning)
1.4.1 Machine learning
1.4.2 Deep learning
1.5 Conclusions/summary
References
2 The rise of artificial intelligence in healthcare applications
2.1 The new age of healthcare
2.1.1 Technological advancements
2.1.2 Artificial intelligence applications in healthcare
2.2 Precision medicine
2.2.1 Genetics-based solutions
2.2.2 Drug discovery and development
2.2.2.1 Drug property and activity prediction
2.2.2.2 De novo design through deep learning
2.2.2.3 Drug–target interactions
2.3 Artificial intelligence and medical visualization
2.3.1 Machine vision for diagnosis and surgery
2.3.1.1 Computer vision for diagnosis and surgery
2.3.2 Deep learning and medical image recognition
2.3.3 Augmented reality and virtual reality in the healthcare space
2.3.3.1 Education and exploration
2.3.3.2 Patient experience
2.4 Intelligent personal health records
2.4.1 Health monitoring and wearables
2.4.2 Natural language processing
2.4.3 Integration of personal records
2.5 Robotics and artificial intelligence-powered devices
2.5.1 Minimally invasive surgery
2.5.2 Neuroprosthetics
2.6 Ambient assisted living
2.6.1 Smart home
2.6.2 Assistive robots
2.6.3 Cognitive assistants
2.6.4 Social and emotional stimulation
2.7 The artificial intelligence can see you now
2.7.1 Artificial intelligence in the near and the remote
2.7.2 Success factors for artificial intelligence in healthcare
2.7.2.1 Assessment of condition
2.7.2.2 Managing complications
2.7.2.3 Patient-care assistance
2.7.2.4 Medical research
2.7.3 The digital primary physician
2.7.3.1 Artificial intelligence prequalification (triage)
2.7.3.2 Remote digital visits
2.7.3.3 The future of primary care
References
3 Drug discovery and molecular modeling using artificial intelligence
3.1 Introduction. The scope of artificial intelligence in drug discovery
3.1.1 Areas in which machine learning techniques are applied in biotechnology
3.2 Various types of machine learning in artificial intelligence
3.2.1 Artificial neural networks as tools in drug discovery
3.2.2 Architecture of artificial neural network for drug discovery applications
3.2.3 Artificial neural network methods for structure prediction in proteins from their sequence
3.2.4 Artificial neural network methods for spectroscopy in biomedicine
3.3 Molecular modeling and databases in artificial intelligence for drug molecules
3.3.1 Databases for the training sets in drug discovery
3.3.2 Database mining
3.4 Computational mechanics ML methods in molecular modeling
3.4.1 Molecular dynamics simulation for drug development
3.4.2 Computations with quantum mechanical techniques for drug development
3.5 Drug characterization using isopotential surfaces
3.6 Drug design for neuroreceptors using artificial neural network techniques
3.7 Specific use of deep learning in drug design
3.7.1 Other applications of machine learning in drug development
3.8 Possible future artificial intelligence development in drug design and development
References
4 Applications of artificial intelligence in drug delivery and pharmaceutical development
4.1 The evolving pharmaceutical field
4.2 Drug delivery and nanotechnology
4.3 Quality-by-design R&D
4.4 Artificial intelligence in drug delivery modeling
4.5 Artificial intelligence application in pharmaceutical product R&D
4.5.1 Artificial intelligence application in prototyping and early development: an example scenario
4.5.2 Artificial intelligence in late-stage development: an example scenario
4.5.2.1 Artificial intelligence in manufacturing development and control
4.6 Landscape of AI implementation in the drug delivery industry
4.7 Conclusion: the way forward
References
5 Cancer diagnostics and treatment decisions using artificial intelligence
5.1 Background
5.2 Artificial intelligence, machine learning, and deep learning in cancer
5.3 Artificial intelligence to determine cancer susceptibility
5.4 Artificial intelligence for enhanced cancer diagnosis and staging
5.5 Artificial intelligence to predict cancer treatment response
5.6 Artificial intelligence to predict cancer recurrence and survival
5.7 Artificial intelligence for personalized cancer pharmacotherapy
5.8 How will artificial intelligence affect ethical practices and patients?
5.9 Concluding remarks
References
6 Artificial intelligence for medical imaging
6.1 Introduction
6.2 Outputs of artificial intelligence in radiology/medical imaging
6.2.1 Preprocessing
6.2.2 Segmentation
6.2.3 Object detection
6.3 Using artificial intelligence in radiology and overcoming its hurdles
6.3.1 Not enough training data (data augmentation)
6.3.2 Unbalanced training data (data weighing)
6.3.3 Not representative training data (transfer learning, domain adaptation)
6.3.3.1 Transfer learning
6.3.3.2 Domain adaptation
6.3.3.2.1 Black box—algorithm explanation
6.3.3.2.2 Implementation/integration
6.4 X-rays and artificial intelligence in medical imaging—case 1 (Zebra medical vision)
6.4.1 X-rays and their role in medicine
6.4.2 X-ray discovery
6.4.3 Chest X-rays
6.4.4 RadBot-CXR—clinical findings using deep learning
6.4.5 Detecting osteoporosis using artificial intelligence
6.5 Ultrasound and artificial intelligence in medical imaging—case 2 (Butterfly iQ)
6.5.1 Ultrasound and its role in medicine
6.5.2 The Butterfly iQ
6.6 Application of artificial intelligence in medical imaging—case 3 (Arterys)
6.7 Perspectives
References
7 Medical devices and artificial intelligence
7.1 Introduction
7.2 The development of artificial intelligence in medical devices
7.2.1 Activity tracking devices
7.2.2 Implants, bionics and robotics devices
7.3 Limitations of artificial intelligence in medical devices
7.4 The future frontiers of artificial intelligence in medical devices
References
8 Artificial intelligence assisted surgery
8.1 Introduction
8.2 Preoperative
8.2.1 Preoperative risk assessment
8.2.2 Preoperative diagnosis
8.2.3 Preoperative staging
8.3 Intraoperative
8.3.1 Autonomous surgery
8.3.2 Computer vision
8.4 Postoperative
8.4.1 Detecting complications
8.4.2 Training and certification of surgeons
8.5 Conclusion
References
Further reading
9 Remote patient monitoring using artificial intelligence
9.1 Introduction to remote patient monitoring
9.2 Deploying patient monitoring
9.2.1 Patient monitoring in healthcare today
9.2.2 Using remote patient monitoring to improve clinical outcomes
9.3 The role of artificial intelligence in remote patient monitoring
9.3.1 Harnessing the power of consumer technology
9.3.2 Sensors, smartphones, apps, and devices
9.3.3 Natural language processing
9.3.4 Natural language processing technology applications in healthcare
9.3.5 Clinical decision support
9.3.6 Ambient assisted living
9.4 Diabetes prediction and monitoring using artificial intelligence
9.4.1 DiaBits
9.4.2 Other diabetes remote monitoring apps and devices
9.5 Cardiac monitoring using artificial intelligence
9.5.1 Virtual application of artificial intelligence in cardiology
9.5.1.1 Imaging interpretation
9.5.1.2 Clinical decision support systems
9.5.1.3 Artificial intelligence in virtual reality, augmented reality, and voice powered virtual ass
9.5.1.4 “BIG DATA” for predictive analysis
9.6 Neural applications of artificial intelligence and remote patient monitoring
9.6.1 Dementia
9.6.1.1 Artificial intelligence in dementia monitoring
9.6.1.2 Smart homes to support dementia patients
9.6.2 Migraine
9.6.2.1 Migraine Buddy
9.6.2.2 Manage My Pain Pro
9.7 Conclusions
References
10 Security, privacy, and information-sharing aspects of healthcare artificial intelligence
10.1 Introduction to digital security and privacy
10.2 Security and privacy concerns in healthcare artificial intelligence
10.2.1 Defining privacy
10.2.2 Privacy and data sharing
10.2.2.1 Toward data interoperability in healthcare
10.2.3 Safety and security in an era of emerging technologies in healthcare
10.2.4 Measures to protect sensitive data in healthcare
10.2.4.1 Traditional techniques
10.2.4.2 Emerging techniques
10.3 Artificial intelligence’s risks and opportunities for data privacy
10.3.1 Artificial intelligence’s risks for data privacy
10.3.1.1 Susceptibility to data privacy and integrity attacks
10.3.1.1.1 Reidentification of patients using artificial intelligence
10.3.1.1.2 Data integrity and bias
10.3.1.1.3 Inadvertent disclosure
10.3.1.2 More limited level of control over data ownership and handling
10.3.1.3 Less intuitive understanding of one’s data privacy
10.3.1.4 Use of sensitive health data outside of healthcare
10.3.1.5 Privacy externalities
10.3.2 Artificial intelligence’s opportunities for data privacy
10.3.2.1 Improving data utility
10.3.2.2 Better attribution of privacy violations
10.3.2.3 Improving physician productivity and data sharing
10.4 Addressing threats to health systems and data in the artificial intelligence age
10.4.1 Addressing cyberattacks and data breaches in healthcare
10.4.2 Government regulation of security and privacy in healthcare
10.5 Defining optimal responses to security, privacy, and information-sharing challenges in healthca
10.5.1 Governments
10.5.1.1 Regulation
10.5.1.2 Oversight and enforcement
10.5.1.3 National strategy development
10.5.2 Innovators
10.5.2.1 Build sustainable solutions
10.5.2.2 Adopting privacy principles
10.5.3 Developing privacy-centered solutions
10.5.4 Providers
10.5.4.1 Internal review
10.5.4.2 Governance
10.5.4.3 Education and training
10.5.4.4 Communication
10.5.5 Patients
10.5.5.1 Data ownership
10.5.5.2 Consent granting
10.6 Conclusions
Acknowledgements
References
11 The impact of artificial intelligence on healthcare insurances
11.1 Overview of the global health insurance industry
11.2 Key challenges facing the health insurance industry
11.2.1 Cost of healthcare
11.2.2 Increased expectations of customers
11.2.3 Regulatory overhead
11.3 The application of artificial intelligence in the health insurance industry
11.3.1 Overview and history of artificial intelligence
11.3.2 Artificial intelligence in health insurance
11.3.2.1 Improving customer experience
11.3.2.2 Reducing fraud
11.3.2.3 Improving back-office efficiency and reducing cost
11.3.2.4 Reducing risk and optimizing premiums
11.3.2.5 Creating new business opportunities
11.4 Case studies
11.4.1 Successes
11.4.2 Failures
11.5 Moral, ethical, and regulatory concerns regarding the use of artificial intelligence
11.5.1 Regulatory challenges
11.5.2 Ethical challenges
11.6 The limitations of artificial intelligence
11.6.1 Lack of talent
11.6.2 Lack of data or poor data quality
11.6.3 Artificial intelligence accuracy, bias, and relevance
11.6.4 Artificial intelligence as part of the decision process
11.6.5 Process control
11.7 The future of artificial intelligence in the health insurance industry
References
12 Ethical and legal challenges of artificial intelligence-driven healthcare
12.1 Understanding “artificial intelligence”
12.2 Trends and strategies
12.2.1 United States
12.2.2 Europe
12.3 Ethical challenges
12.3.1 Informed consent to use
12.3.2 Safety and transparency
12.3.3 Algorithmic fairness and biases
12.3.4 Data privacy
12.4 Legal challenges
12.4.1 Safety and effectiveness
12.4.1.1 United States
12.4.1.1.1 Medical devices
12.4.1.1.2 Medical and certain decision support software
12.4.1.1.2.1 Software functions under Section 520(o)(1)(A)–(D) of the FDCA
12.4.1.1.2.2 Software functions under Section 520(o)(1)(E) of the FDCA
12.4.1.1.3 Other FDA initiatives
12.4.1.2 Europe
12.4.1.2.1 Medical devices and new legal developments
12.4.1.2.2 MDR
12.4.2 Liability
12.4.2.1 United States
12.4.2.2 Europe
12.4.3 Data protection and privacy
12.4.3.1 United States
12.4.3.2 Europe
12.4.4 Cybersecurity
12.4.5 Intellectual property law
12.5 Conclusion

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