Al, Healthcare and Law

Name: Al, Healthcare and Law
Brand: Wiley-ISTE
Price: 142.99 EUR
Availability: OnlineOnly

Guilhem Julia Anne Fauchon Rushed Kanawati(Herausgeber*in)

Wiley-ISTE (Verlag)

1. Auflage

Erschienen am 3. Juli 2024

224 Seiten

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978-1-394-30639-8 (ISBN)

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Inhalt

Cover
Title Page
Copyright Page
Contents
Preface
Introduction
Part 1. Artificial Intelligence to Support Diagnosis
Chapter 1. Healthcare Applications
1.1. The uses of a healthcare application
1.2. Applications at the service of hospitals (HR dimension)
1.2.1. Internal staff and patient management applications in public and private hospitals
1.2.2. Helpful applications to counter isolation and other vulnerabilities
1.3. Cybersecurity to reinforce the resilience of applications
1.3.1. The obligation to protect the health of minor children against content that may harm their health
1.3.2. Assessing health risks through digital services
1.4. Conclusion
1.5. References
Chapter 2. Behavioral Insurance: The Latest Trick of Capitalism?
2.1. Introduction
2.2. A new health insurance market
2.2.1. Healthy lifestyle behavior insurance: an overview
2.2.2. The importance of the institutional context
2.3. Heading towards a great backward leap in health risk socialization?
2.3.1. Artificial intelligence at the service of paternalism in insurance policies
2.3.2. Building on social inequalities
2.4. Conclusion
2.5. References
Chapter 3. Artificial Intelligence and Health: Description of the Ecosystem Required for an Effective Use of AI
3.1. General data ecosystem for data mining and algorithm development
3.1.1. An ecosystem for a digital "revolution"
3.1.2. Some preliminary socio-historical and technical elements
3.1.3. A digital approach from the inside of health.
3.1.4. .and an external approach to health
3.1.5. The evolution of regulatory aspects
3.2. From data to the algorithm at present: the role of research
3.2.1. What kind of research is there for health-related AI nowadays?
3.2.2. Beyond research, the integration of algorithms in the practitioner's environment
3.2.3. An intermediate case for integrating AI into the practitioner's environment: learning systems
3.2.4. Articulation between research and practice
3.3. Examining the quality of reporting of health-related AI research and the readiness of these AI forms through two medical examples
3.3.1. Screening or "predicting" post-traumatic stress disorder with AI
3.3.2. AI to assist the medical examiner in investigation
3.4. Integration and appropriation of algorithms
3.4.1. Technical integration of algorithms, microsocial integration: about appropriation
3.4.2. An example of algorithm integration and socio-technical evaluation: the Big Data Drop IT project
3.4.3. The contribution of mixed methods and the interdisciplinary approach for the end-to-end development of an algorithm - the I-ADViSe project
3.5. The integration of AI in a broader ecosystem than the practitioner's immediate environment: questions and perspectives
3.5.1. The illusions of immediacy and the dematerialized
3.5.2. An opposite problem: what place for health professionals in an ecosystem conducive to the use of AI?
3.5.3. AI as a technical object above all and technology as the common denominator for all our social activities
3.6. References
Part 2. Artificial Intelligence at the Service of Healthcare
Chapter 4. Legal Liability of Companion Robots
4.1. Introduction
4.2. Common law liability
4.2.1. The result of human activity
4.2.2. Caused by the thing
4.3. Special liability regimes
4.3.1. Caused by road accidents
4.3.2. Caused by a defective product
4.4. Conclusion
4.5. References
Chapter 5. From Computer-assisted Surgery to AI-guided Surgery
5.1. Computer-assisted surgery
5.2. Mixed reality, a computer-assisted surgery tool and a key element of AI-guided surgery
5.3. The concept of AI-guided surgery
5.4. Conclusion
5.5. References
Chapter 6. Detection of Anatomical Structures and Lesions in Hand Surgery Through the Use of Artificial Intelligence
6.1. Introduction
6.2. Context
6.2.1. Object detection
6.2.2. Contrastive self-supervised learning
6.3. Problem 1: anatomical structure and lesion detection
6.3.1. Overview of the problem
6.3.2. Modular approach
6.3.3. Separate approach
6.3.4. Evaluation method
6.3.5. Results and opening
6.4. Problem 2: endoscopic carpal tunnel release
6.4.1. Classic approach to object detection
6.4.2. Self-supervised learning approach
6.4.3. Results
6.5. Conclusion
6.6. Appendices
6.7. References
Chapter 7. Surgical Diagnosis Augmented by Artificial Intelligence
7.1. Introduction
7.2. Fundamental framework and state of the art
7.3. Modular learning for classification
7.3.1. Methodology
7.3.2. Results
7.4. Self-learning for data labeling and segmentation
7.4.1. Methodology
7.4.2. Results
7.5. Conclusion
7.6. References
Conclusion
List of Authors
Index
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