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 -- Other titles from ISTE in Science, Society and New Technologies -- EULA.